scientific problem solving adalah

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Pengertian Problem Solving: Aspek, Ciri, dan Langkah-langkahnya 

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KOMPAS.com - Problem solving termasuk soft skill yang harus dimiliki setiap individu, karena memiliki manfaat ketika sudah bekerja di perusahaan. 

Dilansir dari buku Handbook of Cognitive-Behavioral Therapies (3rd Edition) (2010) oleh D'Zurilla dan Nezu, social problem solving adalah suatu proses di mana individu berusaha menangani stres dalam diri, yang juga dapat berfungsi sebagai mediator dalam menangani stres dan tekanan emosional. 

Adapun jenis permasalahan yang digunakan dalam social problem solving , seperti depresi, kecemasan, perilaku bunuh diri, penyakit mental yang berat, putus asa, pesimis, rawan kemarahan, penyalahgunaan zat, kriminal, harga diri yang rendah, stres kerja, dan pelecehan seksual.

Baca juga: Pengertian Problem Solving Menurut Ahli

Aspek kemampuan problem solving  

Menurut Polya dalam bukunya How to Solve It: A New Aspect of Mathematical Method (Second ed) (1973), terdapat empat aspek  kemampuan problem solving , sebagai berikut:

• Memahami masalah

Pemahaman masalah sangat menentukan kesuksesan dalam menemukan solusi masalah. Pada aspek ini melibatkan pendalaman situasi masalah, melakukan pemilahan fakta-fakta, menentukan hubungan di antara fakta-fakta dan membuat formulasi pertanyaan masalah. 

Setiap permasalahan harus dipahami berulang kali dan dipelajari dengan saksama.

• Membuat rencana pemecahan masalah

Rencana solusi masalah dibangun dengan mempertimbangkan struktur masalah dan pertanyaan yang harus dijawab. Pada proses pemecahan masalah siswa dikondisikan memiliki pengalaman dalam menentukan strategi pemecahan masalah.

• Melaksanakan rencana pemecahan masalah

Pada saat mencari solusi yang tepat, rencana yang sudah dibuat harus dilaksanakan dengan hati-hati. Diagram, tabel atau urutan dibangun secara saksama sehingga si pemecah masalah tidak akan bingung. 

Jika muncul ketidak konsistenan ketika melaksanakan rencana, proses harus ditelaah ulang untuk mencari sumber kesulitan masalah.

• Melihat (mengecek) kembali

Selama melakukan pengecekan, solusi masalah tetap di pertimbangkan. Harus tetap cocok terhadap akar masalah meskipun kelihatan tidak beralasan.

Baca juga: Mengenal Individu dengan Karakteristik Self Control

Ciri-ciri problem solving  

Metode problem solving memiliki ciri-ciri, sebagai berikut: 

• Menyiapkan masalah yang jelas untuk diselesaikan

Masalah ini harus tumbuh dari peserta didik sesuai dengan taraf kemampuannya, juga sesuai dengan materi yang disampaikannya. Serta ada dalam kehidupan nyata peserta didik.

• Merumuskan penyelesaian masalah dengan berbagai pendekatan

Mencari data atau keterangan yang dapat memecahkan masalah tersebut. Misalnya dengan membaca buku, meneliti, bertanya, atau pengalaman peserta didik sendiri.

• Menyelesaikan masalah sesuai rencana

Melakukan pembuktian atau pengecekan dari tiap tahap rencana penyelesaian masalah yang telah dirumuskan. Kemudian menjelaskan tahap-tahap penyelesaian dengan benar.

• Memeriksa jawaban yang telah dilakukan dalam penyelesaian masalah

Setelah memeriksa jawaban yang dilakukan dalam penyelesaian masalah, kemudian memberikan penekanan dan menarik kesimpulan atas penyelesaian masalah.

Baca juga: Kegunaan dan Manfaat Self Control dalam kehidupan Sehari-hari

Langkah-langkah kemampuan problem solving  

Disadur dari buku Kurikulum dan Pembelajaran (2013) oleh Oemar Hamalik, ada tujuh langkah kemampuan problem solving secara umum , yaitu: 

• Menghadapi masalah, artinya individu menyadari ada suatu masalah yang dihadapi
• Merumuskan masalah, menjabarkan masalah dengan jelas dan spesifik atau rinci
• Merumuskan hipotesis, merumuskan kemungkinan-kemungkinan jawaban atas masalah tersebut yang masih perlu diuji kebenarannya
• Mengumpulkan dan mengolah data/informasi dengan teknik dan prosedur tertentu
• Menguji hipotesis berdasarkan data/informasi yang telah dikumpulkan dan diolah
• Menarik kesimpulan berdasarkan pengujian hipotesis
• Menerapkan hasil pemecahan masalah situasi baru.

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Tag materi IPS kelas 9 pengertian problem solving adalah social problem solving adalah aspek-aspek problem solving ciri-ciri problem solving langkah kemampuan problem solving secara umum

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Apa Itu Problem Solving? Ini Pengertian, Tujuan, & 5 Metodenya

September 26, 2023

Di masa ini, problem solving adalah salah satu skill yang wajib dimiliki karyawan, terutama pemimpin dan manajer. Ada banyak manfaat problem solving , mulai dari mempermudah pengambilan keputusan hingga meningkatkan efisiensi. Tapi apa itu problem solving sebenarnya? Apa saja skill problem solving yang perlu Anda kuasai?

Dalam bahasan kali ini, kita akan membahas dengan lengkap tentang problem solving , tujuan, manfaat, dan berbagai metodenya. Yuk, scroll ke bawah untuk tahu kelanjutannya!

Apa itu Problem Solving ?

Memahami apa itu problem solving adalah hal fundamental yang harus dipahami siapapun, terutama yang baru masuk ke dunia kerja atau ingin naik jenjang karir. Tanpa pemahaman dan skill problem solving yang mumpuni, seseorang akan mengalami kesulitan saat bekerja, apalagi jika lingkungan kerjanya penuh tekanan.

Menurut buku The Executive Guide to Improvement and Change , pengertian problem solving adalah kemampuan mendefinisikan masalah, menentukan sumbernya, membuat skala prioritas, menyusun alternatif-alternatif solusi, dan mengimplementasikannya sesuai kebutuhan. Singkatnya, problem solving adalah kemampuan menemukan masalah dan memecahkannya dengan baik.

Agar proses pemecahan masalah terlaksana, ada beberapa karakteristik problem solving yang wajib dipenuhi, yaitu:

• Interaksi antara pihak-pihak terlibat, misalnya antar karyawan dalam satu divisi, lintas jabatan, atau antara atasan dan bawahan.
• Terdapat diskusi yang diselenggarakan dengan efektif, sistematis, dan menghasilkan progres, baik secara formal, semiformal, atau informal.
• Informasi lengkap dan valid, penyampai dapat mempertanggungjawabkan kebenarannya.
• Saling membimbing dan melatih dari pihak berpengalaman ke yang kurang berpengalaman.

Berdasarkan karakteristik di atas, kita dapat menemukan bahwa peran pemimpin sangat vital dalam proses pengambilan keputusan. Agar proses problem solving terselesaikan, pemimpin tidak boleh egois atau terlalu longgar pada rekan-rekan yang membantunya mengambil keputusan.

Tujuan Problem Solving

Setelah mengetahui apa itu problem solving , kali ini kita akan membahas beberapa tujuan problem solving dalam perusahaan, di antaranya adalah:

• Melatih kemampuan karyawan untuk menghadapi masalah
• Melatih karyawan dalam menemukan langkah-langkah terbaik untuk mencari solusi dari masalah yang ada
• Melatih karyawan bagaimana cara bertindak dan apa yang harus dilakukan dalam situasi baru
• Melatih karyawan untuk lebih berani dalam mengambil keputusan terbaik
• Melatih karyawan untuk meneliti suatu masalah dari berbagai sudut pandang dan kemungkinan yang ada

Sementara itu, melatih skill problem solving bagi diri sendiri juga sangat penting. Sebab pada faktanya, keahlian ini tidak hanya berguna di dunia kerja, tapi juga dalam aspek-aspek lain kehidupan.

Sebagai contoh, Anda adalah seorang karyawan berusia 24 tahun dengan tanggungan orang tua dan 3 adik. Selain itu, Anda juga punya keinginan punya rumah dan kendaraan di usia 30 tahun. Supaya tanggung jawab dan impian tercapai, Anda melakukan proses problem solving dan menemukan solusi bahwa Anda harus punya side hustle supaya bisa menabung sekaligus tetap membantu ekonomi keluarga.

BACA JUGA: Manfaat Menerapkan Teamwork Karyawan di Perusahaan Anda

  Tahapan Problem Solving

Setelah memahami apa itu problem solving dan tujuannya, di bawah ini terdapat beberapa tahapan untuk menerapkan metode problem solving . Jika Anda merasa belum punya skill problem solving mumpuni, cara-cara di bawah ini dapat membantu Anda berlatih.

1. Mendefinisikan Masalah

Tahapan pertama problem solving adalah dengan mendefinisikan, mengurai, dan menyusun kembali satu per satu masalah pokok yang sedang terjadi. Meskipun masalah-masalah tersebut tampak banyak, usahakan untuk menemukan inti dari semua masalah tersebut.

Jika Anda sedang bekerja di perusahaan, pastikan untuk mengajak rekan kerja dan orang lain yang berhubungan dengan masalah tersebut. Dengan demikian, Anda dapat mendengar masalah dari berbagai perspektif dan menemukan titik masalah.

2. Menentukan Sumber/Dalang Penyebab Masalah

Setelah masalah utama ditemukan, tahapan selanjutnya problem solving adalah menyelidiki sumber masalah tersebut. Apakah masalah timbul karena sistem? Orang-orang terlibat? Atau komunikasi yang kurang efektif? Dengan menemukan jawaban dari pertanyaan semacam itu, Anda dan tim dapat melakukan brainstorming sumber masalah, sebelum mencari solusinya.

3. Menentukan Prioritas Masalah

Dalam satu kali brainstorming , Anda dan rekan-rekan barangkali akan menemukan lebih dari satu masalah untuk dipecahkan. Namun demikian, memaksakan diri menyelesaikan semua masalah dalam satu waktu sangat tidak efisien. Bukannya tuntas, bisa-bisa Anda dan tim justru tidak akan memecahkan satu pun masalah.

4. Mengembangkan Solusi Alternatif

Claire Cook – penulis terkenal asal Amerika Serikat – pernah berkata, “Jika plan A tidak berhasil, ingatlah masih ada 25 huruf untuk dijadikan rencana ( plan B, C, D, dan seterusnya”. Alternatif-alternatif rencana seperti ini juga perlu Anda siapkan jika sewaktu-waktu solusi utama tidak bekerja.

5. Mengimplementasikan Solusi dan Mengevaluasinya

Tahapan terakhir pada proses problem solving adalah mengimplementasikan solusi sesuai kesepakatan bersama. Setelah sudah menemukan solusi terbaik, maka Anda tinggal menyusun strategi penerapan, membagikannya kepada tim anggota, dan menindaklanjuti solusi yang sudah diputuskan.

Tidak berhenti sampai disitu, ada baiknya jika Anda bisa mengumpulkan masukan dari anggota tim atau pihak-pihak yang terlibat dan melakukan evaluasi dari penerapan solusi tersebut.

Pada setiap tahapan untuk menyelesaikan masalah, dibutuhkan beberapa skill problem solving yang mumpuni. Seperti kemampuan menganalisis, kemampuan berdiskusi, hingga penentuan prioritas.

BACA JUGA: Jenis Kepemimpinan Dalam Perusahaan. Anda Termasuk yang Mana?

Metode Problem Solving

Dalam proses problem solving , ada beberapa metode yang dapat Anda gunakan, di antaranya adalah:

1. Linear Thinking

Metode problem solving pertama yang dapat Anda terapkan adalah linear thinking . Penggunaan metode ini sangat sederhana, yaitu dengan menekankan pada pertanyaan “mengapa” agar bisa menemukan akar permasalahan. Setelah akarnya ditemukan, Anda bisa menggunakan data-data lama dan solusi yang ada untuk diterapkan.

Linear thinking adalah salah satu metode problem solving paling tradisional dan mudah dilaksanakan. Kelemahannya, linear thinking hanya cocok untuk menghadapi masalah yang pernah dihadapi sebelumnya, tapi tidak sesuai jika masalahnya sama sekali baru.

2. Design Thinking

Berbeda dengan linear thinking , dalam apa itu problem solving penggunaan design thinking lebih menekankan pendekatan dari sisi user . Untuk memulainya Anda bisa mencoba untuk berempati kepada user yang sedang menghadapi masalah.

Kemudian setelah Anda mengetahui apa masalah yang dihadapinya, Anda bisa menggunakan skill problem solving yang dimiliki untuk membuat beberapa gambaran atau prototype yang dapat diuji untuk menemukan solusi dari masalah tersebut.

3. Creative Problem Solving

Ketika kita membahas apa itu problem solving , maka Anda perlu menciptakan keseimbangan antara logika dan kreativitas. Anda bisa menggunakan kreativitas untuk mencari tahu apa penyebab masalah yang terjadi dan kemudian mengembangkan solusi yang inovatif.

Metode creative problem solving tidak hanya seputar brainstorming atau ide-ide gila yang out of the box . Tetapi Anda juga perlu fokus untuk mendapatkan ide sebanyak-banyaknya dari proses tersebut.

4. Solution-based Thinking

Metode problem solving keempat yang dapat Anda terapkan adalah solution-based thinking , yaitu metode pemecahan masalah dengan berfokus pada solusi-solusi yang dapat dipastikan keberhasilannya.

Jika dibandingkan, solution-based thinking tampak seperti pertengahan antara linear thinking dan creative problem solving . Dari segi kecepatan, metode solution-based sama terfokusnya seperti linear thinking . Akan tetapi, dari segi fleksibilitas ide, solution-based thinking menggunakan pendekatan brainstorming seperti creative problem solving .

Demikianlah penjelasan mengenai apa itu problem solving , tujuan, dan metode-metodenya. Skill problem solving adalah salah satu keahlian paling dicari di dunia kerja. Bagi perusahaan, karyawan dengan kemampuan memecahkan masalah adalah aset berharga, baik untuk masa sekarang atau masa depan.

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Pengertian Problem Solving Beserta Teori dan Contoh Soalnya

• Posted by by Maulia Indriana Ghani
• Mei 10, 2022

Elo pernah main game tebak-tebakan, nggak? Misalnya, ada tiga orang, manakah yang termasuk pencuri? Nah, itu termasuk contoh problem solving. Apa pengertian problem solving? Gimana strategi penyelesaiannya? Yuk, kepoin!

Elo termasuk pencinta kopi, bukan? Biasanya, pencinta kopi itu kalau pagi-pagi sebelum beraktivitas, ya ngopi dulu. Kalau nggak ngopi, rasanya bakal lemas sepanjang hari, nggak bergairah.

Alhasil, kegiatan membuat kopi itu menjadi sesuatu yang elo lakukan secara otomatis tanpa proses berpikir panjang. Pokoknya langsung satsetsatset . Mulai dari menyiapkan cangkir, menuang kopi ke dalam cangkir, menambahkan gula, menuang air panas, mengaduk-aduk, dan yang terakhir, seruput, deh!

Lain halnya ketika elo mau membuat kopi ala coffee shop , misalnya latte art . Buat elo yang nggak biasa bikin latte art , kegiatan tersebut tentu membutuhkan proses berpikir, yang mencakup strategi dan perencanaan.

Misalnya, apa aja sih, yang gue butuhkan untuk membuat latte art ? Oh, gue butuh alatnya, bahan-bahan harus yang terbaik, lama proses pembuatannya juga perlu gue perhatikan supaya nggak telat berangkat sekolah, terakhir bentuk art -nya.

Kurang lebih, elo akan berpikir seperti itu, kan? Jadi, dalam menyelesaikan masalah atau problem solving itu elo akan menggunakan metode yang berbeda-beda. Misalnya pada contoh kasus kopi di atas, elo menggunakan metode planning perincian detail.

Kedua, ada metode perhitungan matematis. Jadi, elo menggunakan perhitungan dalam menyelesaikan suatu masalah. Selanjutnya, ada metode trial-error , elo coba, gagal, elo ulang lagi sampai berhasil.

Nah, cara terbaik untuk solve problem adalah elo harus tahu konteks masalah dan informasi yang elo punya terlebih dahulu untuk mendapatkan metode yang paling cocok digunakan. Namun, elo nggak harus memilih salah satu dari ketiga cara tersebut, kok. Elo bisa mengombinasikan ketiga cara tersebut untuk mendapatkan solusi yang terbaik.

Oke, contohnya bakal gue bahas setelah elo memahami pengertian problem solving di bawah ini, ya.

Apa Itu Problem Solving?

Elo pasti sering mendengar istilah problem solving , kan? Di sekolah pun kita dididik untuk memiliki skill yang satu ini. Nggak cuma di sekolah, kok. Dunia kerja pun membutuhkan orang-orang dengan skill tersebut.

Pasalnya, problem solving adalah bagian dari keterampilan atau kecakapan intelektual seseorang. Tanpa memahami dan memiliki skill tersebut, akan sulit rasanya saat elo menghadapi berbagai masalah atau hambatan dalam hidup.

Kita bisa mendefinisikan pengertian problem solving sebagai proses identifikasi masalah, mengembangkan solusi yang mungkin bisa digunakan, dan mengambil tindakan yang tepat dari pilihan solusi tersebut.

Oke, sekarang kita tahu nih, kalau problem solving itu secara istilah use logic atau menggunakan logika berpikir dan prosedur efektif untuk menyelesaikan suatu masalah setepat dan sesimpel mungkin.

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Jadi, jelas ya, bahwa tujuan problem solving itu untuk memecahkan suatu masalah. Selain itu, untuk melatih orang-orang dalam menghadapi permasalahan dan hambatan, mendapatkan langkah terbaik untuk menyelesaikan permasalahan, dan melatih orang untuk bertindak di situasi baru.

Ada nggak sih, pengertian problem solving secara teoritis? Ada. Teori problem solving yang akan gue angkat kali ini berdasarkan pendapat Marzano dkk (1988), bahwa problem solving adalah salah satu bagian dari proses berpikir yang berupa kemampuan untuk memecahkan permasalahan.

Nah, kalau di sekolah, tujuan problem solving ini untuk memecahkan masalah dalam pelajaran matematika, sains, dan ilmu sosial. Contohnya gimana, sih? Penasaran? Oke, lanjut ke poin berikutnya, ya.

Strategi Problem Solving

Coba deh, elo perhatikan soal dan penyelesaiannya di bawah ini!

Gimana, kebayang nggak sama cara di atas? Gue rincikan penyelesaiannya supaya elo bisa lebih mudah dalam memahaminya, ya.

Pertama, elo perhatikan dulu data yang disajikan. Dari data tersebut, elo bisa memperoleh informasi penting atau aturan-aturan suatu masalah. Ingat, bahwa aturan itu untuk elo perhatikan dan ikuti, bukan kontradiksi atau kebalikan dari aturan itu, ya!

Baca Juga : Mengenal Kesalahan Logika Beban Pembuktian

Selanjutnya, elo proses dan analisis datanya hingga menghasilkan solusi.

Dari contoh kasus tersebut, kita memperoleh satu hal penting. Hal penting apa, sih? Dari situ kita belajar, bahwa untuk memecahkan masalah secara tepat, kita perlu mengikuti serangkaian tahapan.

Kita bisa menyebut rangkaian tahapan tersebut sebagai strategi problem solving . Ada yang gue suka, nih. Bransford dan Stein (1993), memperkenalkan strategi problem solving dengan akronim IDEAL.

IDEAL = Identify, Define, Explore, Act dan Look

Gue uraikan satu per satu, ya.

I → Identify Problem

Pada tahap ini, elo perlu mengidentifikasi masalahnya terlebih dahulu. Karena, masalah itu kadang nggak sesederhana itu, guys.

Dalam beberapa kasus, orang-orang mungkin saja salah menafsirkan atau mengidentifikasikan masalah. Alhasil, upaya problem solving yang dilakukan nggak seefektif dan seefisien yang diharapkan, iya nggak?

Strategi yang bisa elo gunakan, misalnya dengan mengajukan pertanyaan mengenai masalah tersebut, cari tahu seluk-beluk permasalahan itu—bisa menjawab apa, siapa, mengapa, kapan, di mana, dan bagaimana.

Elo juga bisa memecah atau mengklasifikasikan permasalahan menjadi bagian yang lebih kecil. Lihat juga masalah itu dari berbagai sudut pandang. Kalau udah, elo bisa lanjut ke tahap selanjutnya.

D → Define Goal

Setelah identifikasi masalah, elo juga perlu mendefinisikan suatu masalah secara detail. Untuk apa? Tentu saja untuk dapat solve problem tersebut.

Cari tahu aspek mana sih, yang termasuk fakta, dan mana yang termasuk opini. Bedakan hal itu. Kemudian, definisikan masalah secara jelas dan identifikasi solusinya.

E → Explore Possible Strategies

Selanjutnya, gali solusinya. Manakah solusi yang paling potensial untuk memecahkan masalah tersebut?

Di tahap ini, elo perlu mengumpulkan banyak ide, sebanyak-banyaknya, ya.

Kalau udah ada banyak ide, langkah selanjutnya adalah mengembangkan strategi. Elo bisa menggunakan strategi heuristik, yaitu menemukan solusi berdasarkan pengalaman masa lalu yang mirip dengan masalah sekarang.

Atau menggunakan strategi algoritma, yaitu menemukan solusi dengan cara bertahap untuk mendapatkan solusi yang lebih akurat. Namun, tentu saja strategi algoritma lebih lama, karena elo harus merinci lebih detail dalam menyelesaikan masalahnya.

A → Anticipate Outcomes and Act

Setelah strategi tertentu dipilih, elo mulai melaksanakan strategi tersebut di tahap ini. Kira-kira, strategi yang udah gue pilih ini akan berhasil atau nggak, ya? Langkah ini sudah betul atau belum, ya? Efektif atau nggak, ya?

Selain menggunakan strategi, elo juga masih perlu memantau situasi. Pastikan bahwa masalah yang sedang diselesaikan sekarang itu nggak menimbulkan masalah baru.

L → Look back and Learn

Setelah solusi tercapai, bukan berarti elo bisa melenggang pergi gitu aja, ya. Kaji kembali solusi yang sudah dilaksanakan dan evaluasi dampaknya.

Kalau di sekolah, setelah elo menyelesaikan suatu soal, misalnya matematika, elo cek lagi hasilnya. Perhitungan elo udah benar atau ada yang keliru? Elo udah menggunakan cara yang tepat atau belum? Elo tadi baca soalnya teliti atau nggak? Begitu, kan?

Kalau semuanya sudah oke, artinya elo berhasil menyelesaikan suatu masalah. Kalau masih belum berhasil, elo coba lagi, ulang dari awal. Artinya, elo sedang menggunakan metode trial-error .

Gimana, paham sampai sini? Kalau elo masih kurang greget sama uraian di atas, jangan khawatir. Karena, elo bisa pelajari materi problem solving pakai animasi di video belajar Zenius dengan klik banner di bawah ini.

Contoh Soal Problem Solving dan Pembahasan

Setelah memahami uraian mengenai pengertian problem solving di atas, artinya elo udah siap menyelesaikan berbagai permasalahan dari soal-soal di bawah ini. Cekidot !

Contoh Soal 1

Zahra mengikuti acara amal dan ia kebagian mengumpulkan amplop-amplop yang berisi uang dari penyumbang. Amplop-amplop tersebut berisi uang kertas. Semua amplopnya berisi tiga uang kertas, namun ada juga beberapa amplop yang berisi satu, dua atau tiga nota (bukan uang). Semua uang kertas bisa bernilai Rp1.000, Rp5.000, Rp10.000, atau Rp20.000. Berapa jumlah uang terkecil yang nggak mungkin ada di dalam sebuah amplop?

A. Rp2.000.

B. Rp3.000.

C. Rp4.000.

D. Rp6.000.

E. Rp7.000.

Jawab: C. Rp4.000 .

Pembahasan:

Dari bacaan, kita peroleh kemungkinan-kemungkinan munculnya jumlah uang.

• Tiga uang = 3U.
• Satu nota bukan uang (artinya ada dua uang) = 2U + 1N.
• Dua nota bukan uang (artinya ada satu uang) = 1U + 2N.
• Tiga nota = 3N.

Uang yang ada di dalam amplop senilai Rp1.000, Rp5.000, Rp10.000, atau Rp20.000.

Nah, ditanyakan jumlah uang terkecil yang nggak mungkin ada dalam amplop. Kita coba satu per satu pilihan ganda di atas, berdasarkan aturan dari poin-poin yang udah dibuat ya.

Opsi A → Rp2.000.

Kita bisa peroleh dari 2U + 1N = Rp1.000 + Rp1.000 + nota = Rp2.000. Jadi, bukan opsi A jawabannya, ya.

Opsi B → Rp3.000.

Kita bisa memperolehnya dari 3U = Rp1.000 + Rp1.000 + Rp1.000 = Rp3.000. Jadi, bukan opsi B jawabannya, ya.

Opsi C → Rp4.000.

Kita coba satu per satu. Dimulai dari 3U dulu, ya. 3U akan menghasilkan Rp3.000, Rp7.000, dan seterusnya yang jumlahnya akan semakin besar. Nggak mungkin.

2U + 1N akan menghasilkan Rp2.000, Rp6.000, dan seterusnya.

1U + 2N akan menghasilkan Rp1.000, Rp5.000, dan seterusnya.

Artinya, kita nggak bisa memperoleh uang total Rp4.000 di dalam amplop. Jawabannya C, ya.

Penasaran sama opsi lainnya? Udah ketemu jawabannya, opsi D menghasilkan Rp6.000, ada ya dari 2U + 1N. Kemudian, opso E yaitu Rp7.000 diperoleh dari 3U. Kemungkinan, ada amplop yang totalnya Rp6.000 dan Rp7.000.

Jadi, jumlah uang terkecil yang nggak mungkin ada di dalam sebuah amplop adalah Rp4.000.

Contoh Soal 2

Perhatikan gambar di bawah ini!

Kalau kita lihat dari gambar bus di Indonesia yang sedang melaju di jalanan, kira-kira bus tersebut melaju ke arah kanan atau kiri?

Gue tantang elo untuk menjawab pertanyaan di atas. Ada yang bisa jawab, nggak?

Ayo, belajar jadi detektif! Elo identifikasi kasus di atas, kemudian cari strategi dan solusi yang paling tepat untuk menyelesaikan permasalahannya. Kalau udah, cantumkan jawaban elo di kolom komentar, ya!

Kalau bingung atau mau intip pembahasannya, elo bisa meluncur ke video contoh soal dan pembahasan problem solving teka-teki di sini .

Wah, nggak kerasa bahasan kita udah di ujung, nih. Sampai sini udah paham tentang pengertian problem solving, teori, tujuan, strategi, dan contoh soalnya? Kalau elo lebih suka belajar dengan nonton video, elo bisa mengakses materi UTBK lainnya di video Zenius. Elo juga bisa mencoba melatih kemampuan dengan level soal yang mirip UTBK beneran di Try Out bareng Zenius .

Kalau elo mau berlatih mengerjakan berbagai soal menarik, gampang banget! Elo bisa segera langganan paket Zenius dengan klik gambar di bawah ini!

Baca Juga : Panduan Belajar dan Soal Pola Gambar UTBK TPS/TPA

Overview of the Problem-Solving Mental Process — Verywell Mind (2022).

Problem Solving : Signifikansi, Pengertian, dan Ragamnya — Satya Widya, Vol 28, No. 2 (2012).

Pembelajaran Matematika Model Ideal Problem Solving dengan Teori Pemrosesan Informasi Untuk Pembentukan Pendidikan Karakter dan Pemecahan Masalah Materi Dimensi Tiga Kelas X SMA — Pythagoras, Vol. 7, No. 2 (2012).

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Article • 5 min read

Using the Scientific Method to Solve Problems

How the scientific method and reasoning can help simplify processes and solve problems.

By the Mind Tools Content Team

The processes of problem-solving and decision-making can be complicated and drawn out. In this article we look at how the scientific method, along with deductive and inductive reasoning can help simplify these processes.

‘It is a capital mistake to theorize before one has information. Insensibly one begins to twist facts to suit our theories, instead of theories to suit facts.’ Sherlock Holmes

The Scientific Method

The scientific method is a process used to explore observations and answer questions. Originally used by scientists looking to prove new theories, its use has spread into many other areas, including that of problem-solving and decision-making.

The scientific method is designed to eliminate the influences of bias, prejudice and personal beliefs when testing a hypothesis or theory. It has developed alongside science itself, with origins going back to the 13th century. The scientific method is generally described as a series of steps.

• observations/theory
• explanation/conclusion

The first step is to develop a theory about the particular area of interest. A theory, in the context of logic or problem-solving, is a conjecture or speculation about something that is not necessarily fact, often based on a series of observations.

Once a theory has been devised, it can be questioned and refined into more specific hypotheses that can be tested. The hypotheses are potential explanations for the theory.

The testing, and subsequent analysis, of these hypotheses will eventually lead to a conclus ion which can prove or disprove the original theory.

Applying the Scientific Method to Problem-Solving

How can the scientific method be used to solve a problem, such as the color printer is not working?

1. Use observations to develop a theory.

In order to solve the problem, it must first be clear what the problem is. Observations made about the problem should be used to develop a theory. In this particular problem the theory might be that the color printer has run out of ink. This theory is developed as the result of observing the increasingly faded output from the printer.

2. Form a hypothesis.

Note down all the possible reasons for the problem. In this situation they might include:

• The printer is set up as the default printer for all 40 people in the department and so is used more frequently than necessary.
• There has been increased usage of the printer due to non-work related printing.
• In an attempt to reduce costs, poor quality ink cartridges with limited amounts of ink in them have been purchased.
• The printer is faulty.

All these possible reasons are hypotheses.

3. Test the hypothesis.

Once as many hypotheses (or reasons) as possible have been thought of, then each one can be tested to discern if it is the cause of the problem. An appropriate test needs to be devised for each hypothesis. For example, it is fairly quick to ask everyone to check the default settings of the printer on each PC, or to check if the cartridge supplier has changed.

4. Analyze the test results.

Once all the hypotheses have been tested, the results can be analyzed. The type and depth of analysis will be dependant on each individual problem, and the tests appropriate to it. In many cases the analysis will be a very quick thought process. In others, where considerable information has been collated, a more structured approach, such as the use of graphs, tables or spreadsheets, may be required.

5. Draw a conclusion.

Based on the results of the tests, a conclusion can then be drawn about exactly what is causing the problem. The appropriate remedial action can then be taken, such as asking everyone to amend their default print settings, or changing the cartridge supplier.

Inductive and Deductive Reasoning

The scientific method involves the use of two basic types of reasoning, inductive and deductive.

Inductive reasoning makes a conclusion based on a set of empirical results. Empirical results are the product of the collection of evidence from observations. For example:

‘Every time it rains the pavement gets wet, therefore rain must be water’.

There has been no scientific determination in the hypothesis that rain is water, it is purely based on observation. The formation of a hypothesis in this manner is sometimes referred to as an educated guess. An educated guess, whilst not based on hard facts, must still be plausible, and consistent with what we already know, in order to present a reasonable argument.

Deductive reasoning can be thought of most simply in terms of ‘If A and B, then C’. For example:

• if the window is above the desk, and
• the desk is above the floor, then
• the window must be above the floor

It works by building on a series of conclusions, which results in one final answer.

Social Sciences and the Scientific Method

The scientific method can be used to address any situation or problem where a theory can be developed. Although more often associated with natural sciences, it can also be used to develop theories in social sciences (such as psychology, sociology and linguistics), using both quantitative and qualitative methods.

Quantitative information is information that can be measured, and tends to focus on numbers and frequencies. Typically quantitative information might be gathered by experiments, questionnaires or psychometric tests. Qualitative information, on the other hand, is based on information describing meaning, such as human behavior, and the reasons behind it. Qualitative information is gathered by way of interviews and case studies, which are possibly not as statistically accurate as quantitative methods, but provide a more in-depth and rich description.

The resultant information can then be used to prove, or disprove, a hypothesis. Using a mix of quantitative and qualitative information is more likely to produce a rounded result based on the factual, quantitative information enriched and backed up by actual experience and qualitative information.

In terms of problem-solving or decision-making, for example, the qualitative information is that gained by looking at the ‘how’ and ‘why’ , whereas quantitative information would come from the ‘where’, ‘what’ and ‘when’.

It may seem easy to come up with a brilliant idea, or to suspect what the cause of a problem may be. However things can get more complicated when the idea needs to be evaluated, or when there may be more than one potential cause of a problem. In these situations, the use of the scientific method, and its associated reasoning, can help the user come to a decision, or reach a solution, secure in the knowledge that all options have been considered.

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04 Mar 2022

Apa itu problem solving manfaat dan penerapannya.

Artikel - FAS,

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Masalah dapat didefinisikan sebagai situasi atau tantangan yang memerlukan tindakan atau pemecahan untuk mencapai tujuan yang diinginkan. Dalam hal ini, masalah dapat didefinisikan sebagai proses kognitif yang melibatkan identifikasi, pemahaman, dan penyelesaian suatu masalah.

Proses penyelesaian masalah dimulai dengan pengenalan masalah, kemudian analisis masalah untuk mengetahui penyebabnya dan solusi yang mungkin. Setelah itu, langkah-langkah konkret diambil untuk menerapkan solusi tersebut, dan hasilnya dievaluasi untuk memastikan bahwa masalah telah diselesaikan secara efektif.

Dalam penyelesaian masalah, berbagai keterampilan dapat diperlukan, termasuk kreativitas, pemikiran kritis, pengambilan keputusan, dan kemampuan untuk membangun dan menguji solusi. Ini adalah proses penting dalam kehidupan sehari-hari, baik dalam konteks profesional maupun pribadi. Kemampuan untuk mengatasi masalah dengan efektif dapat membantu seseorang mengatasi masalah, mencapai tujuan, dan membuat keputusan yang lebih baik.

Bagaimana Proses Problem Solving Terjadi?

Untuk mengatasi masalah atau situasi tantangan, seringkali seseorang menggunakan proses penyelesaian masalah. Pada tahap pertama, masalah diidentifikasi. Ini berarti masalah dikenali dengan jelas. Setelah itu, analisis masalah dilakukan untuk memahami sumber masalah, serta akibatnya. 

Pada tahap ketiga, ide kreatif digunakan untuk menghasilkan berbagai alternatif solusi. Setelah itu, evaluasi solusi dilakukan untuk menentukan solusi terbaik berdasarkan hasilnya. Tahap berikutnya adalah menerapkan solusi melalui rencana tindakan yang jelas, dan terakhir, evaluasi hasilnya. 

Proses penyelesaian masalah membantu orang mengatasi masalah dengan cara yang terorganisir dan efektif, menghasilkan solusi yang lebih baik, dan membuat keputusan yang lebih baik.

Manfaat Problem Solving

Delapan berikut adalah manfaat utama dari memiliki kemampuan menyelesaikan masalah yang perlu kamu tau:

1. Peningkatan Kemampuan Pemecahan Masalah  

Manfaat utama problem solving adalah kemampuan untuk mengatasi masalah dengan lebih efektif. Seseorang yang telah memiliki kemampuan pemecahan masalah akan dapat menghadapi tantangan dengan lebih percaya diri, mencari solusi yang lebih baik, dan mengurangi tingkat stres yang dihadapi ketika menghadapi masalah.

2. Meningkatkan Kemampuan Pengambilan Keputusan

Proses analisis dan evaluasi yang dikenal sebagai penyelesaian masalah membantu orang membuat keputusan yang lebih baik dalam kehidupan pribadi dan profesional, seperti memilih karir, investasi, atau keputusan-keputusan penting lain dalam hidup.

3. Meningkatkan Kreativitas 

Saat menghadapi masalah, seseorang seringkali harus berpikir kreatif untuk menemukan cara baru untuk menyelesaikannya. Hal ini dapat membantu meningkatkan kemampuan kreatif dan inovasi.

4. Meningkatkan Komunikasi 

Untuk meningkatkan kemampuan komunikasi interpersonal, penyelesaian masalah sering melibatkan kerja tim, di mana orang harus berkomunikasi dan bekerja sama dengan orang lain.

5. Meningkatkan Produktivitas

Dengan memecahkan masalah secara efektif, individu dan kelompok dapat meningkatkan produktivitas dan efisiensi, yang berkontribusi pada pencapaian tujuan dan hasil yang diinginkan.

6. Meningkatkan Kepercayaan Diri 

Mengatasi masalah dengan sukses dapat meningkatkan kepercayaan diri seseorang. Ini karena mereka sadar bahwa mereka memiliki kemampuan untuk menghadapi tantangan.

7. Pengembangan Karier

Dalam konteks karir, kemampuan pemecahan masalah sangat dihargai. Orang yang memiliki kemampuan pemecahan masalah yang baik memiliki kemungkinan lebih besar untuk mencapai kesuksesan di tempat kerja.

8. Meningkatkan Kualitas Hidup 

Kemampuan menyelesaikan masalah dapat meningkatkan kualitas hidup seseorang. Ini karena kemampuan pemecahan masalah memungkinkan orang untuk mengatasi masalah yang mungkin menghalangi mereka dari mencapai tujuan dan kebahagiaan pribadi mereka.

Oleh karena itu, mempelajari kemampuan menyelesaikan masalah adalah langkah yang bagus untuk membangun diri sendiri dan meningkatkan kualitas hidup secara keseluruhan.

Penerapan Problem Solving di Kehidupan

Dalam kehidupan sehari-hari, memecahkan masalah berarti mengatasi berbagai situasi dan masalah. Pertama-tama, penting untuk mengidentifikasi masalah dengan jelas. Ini berarti merumuskan masalah dengan tepat, menemukan sumbernya, dan memahami bagaimana masalah tersebut akan mempengaruhi kehidupan kita. Misalnya, beban kerja yang berlebihan adalah masalah jika seseorang mengalami stres karena terlalu banyak tugas yang harus mereka selesaikan.

Analisis dilakukan setelah masalah ditemukan. Ini mencakup mengumpulkan informasi, memikirkan solusi yang mungkin, dan memahami akibat dari setiap solusi. Orang mungkin perlu mempertimbangkan contoh di atas atau meminta bantuan rekan kerja.

Selanjutnya, langkah ketiga adalah membuat dan menerapkan solusi. Ini mencakup membuat rencana tindakan yang jelas, mengambil tindakan konkrit untuk mengatasi masalah, dan dengan konsisten mengikuti rencana tersebut. Mengatur prioritas tugas, menggunakan alat manajemen waktu, atau berbicara dengan atasan tentang cara memberikan tugas yang lebih seimbang adalah beberapa solusi untuk beban kerja yang berlebihan.

Terakhir, refleksi dan evaluasi adalah langkah penting dalam menyelesaikan masalah. Setelah penerapan solusi, sangat penting untuk menilai apakah masalah telah diselesaikan dengan baik dan apakah solusi itu efektif. Jika hasil yang diinginkan belum dicapai, orang harus siap untuk merevisi rencana dan mencari solusi yang lebih baik atau perbaikan.

Problem solving membantu orang mengatasi masalah dengan lebih baik, mengurangi stres, meningkatkan kualitas hidup, dan membuat keputusan yang lebih baik. Ini juga membantu mereka tumbuh dalam keterampilan penting yang mereka miliki secara pribadi dan profesional. Problem solving dapat menjadi alat yang kuat untuk menghadapi masalah dalam kehidupan sehari-hari jika dilakukan dengan cara yang sistematis dan berpikir kritis.

Sampoerna University

Sampoerna University adalah sebuah universitas terakreditasi penuh di Indonesia yang menawarkan pilihan terbaik bagi mereka yang mencari pendidikan internasional unggul. Kami adalah universitas swasta, non-denominasi, nirlaba yang berlisensi dan terakreditasi oleh Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia. 

Sampoerna University menawarkan berbagai program sarjana dan magister di bidang-bidang seperti bisnis , teknologi informasi , kreativitas dan desain , serta studi kelas dunia. Universitas ini menempatkan fokus pada pendekatan pembelajaran yang inovatif dan berorientasi pada industri, dengan tujuan untuk mempersiapkan mahasiswa berhasil dalam karir mereka.

Kami berkomitmen untuk menyediakan lingkungan pembelajaran yang inklusif dan mendukung bagi mahasiswa, dengan dukungan fasilitas modern dan fakultas yang berkualitas. Kami juga memberikan beasiswa dan program bantuan keuangan untuk mendukung aksesibilitas pendidikan bagi mahasiswa berprestasi.

Dalam beberapa tahun sejak didirikan, Sampoerna University telah menjadi pilihan pendidikan tinggi yang menarik bagi calon mahasiswa di Indonesia. Dengan pendekatan pembelajaran yang inovatif, koneksi industri yang kuat, dan fokus pada pengembangan karir, kami memiliki tujuan untuk menghasilkan lulusan yang siap menghadapi tantangan dunia kerja.

Segera daftar untuk ikut proses penerimaan mahasiswa baru tahun ajaran 2023-2024 disini . Admission Team kami akan segera menghubungi untuk memberi informasi lebih detail.

Jadwalkan dengan kami kapanpun kamu ingin visit tour kampus on-site atau virtual!

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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1.2: Scientific Approach for Solving Problems

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Learning Objectives

• To identify the components of the scientific method

Scientists search for answers to questions and solutions to problems by using a procedure called the scientific method . This procedure consists of making observations, formulating hypotheses, and designing experiments, which in turn lead to additional observations, hypotheses, and experiments in repeated cycles (Figure \(\PageIndex{1}\)).

Observations can be qualitative or quantitative. Qualitative observations describe properties or occurrences in ways that do not rely on numbers. Examples of qualitative observations include the following: the outside air temperature is cooler during the winter season, table salt is a crystalline solid, sulfur crystals are yellow, and dissolving a penny in dilute nitric acid forms a blue solution and a brown gas. Quantitative observations are measurements, which by definition consist of both a number and a unit. Examples of quantitative observations include the following: the melting point of crystalline sulfur is 115.21 °C, and 35.9 grams of table salt—whose chemical name is sodium chloride—dissolve in 100 grams of water at 20 °C. An example of a quantitative observation was the initial observation leading to the modern theory of the dinosaurs’ extinction: iridium concentrations in sediments dating to 66 million years ago were found to be 20–160 times higher than normal. The development of this theory is a good exemplar of the scientific method in action (see Figure \(\PageIndex{2}\) below).

After deciding to learn more about an observation or a set of observations, scientists generally begin an investigation by forming a hypothesis , a tentative explanation for the observation(s). The hypothesis may not be correct, but it puts the scientist’s understanding of the system being studied into a form that can be tested. For example, the observation that we experience alternating periods of light and darkness corresponding to observed movements of the sun, moon, clouds, and shadows is consistent with either of two hypotheses:

• Earth rotates on its axis every 24 hours, alternately exposing one side to the sun, or
• The sun revolves around Earth every 24 hours.

Suitable experiments can be designed to choose between these two alternatives. For the disappearance of the dinosaurs, the hypothesis was that the impact of a large extraterrestrial object caused their extinction. Unfortunately (or perhaps fortunately), this hypothesis does not lend itself to direct testing by any obvious experiment, but scientists collected additional data that either support or refute it.

After a hypothesis has been formed, scientists conduct experiments to test its validity. Experiments are systematic observations or measurements, preferably made under controlled conditions—that is, under conditions in which a single variable changes. For example, in the dinosaur extinction scenario, iridium concentrations were measured worldwide and compared. A properly designed and executed experiment enables a scientist to determine whether the original hypothesis is valid. Experiments often demonstrate that the hypothesis is incorrect or that it must be modified. More experimental data are then collected and analyzed, at which point a scientist may begin to think that the results are sufficiently reproducible (i.e., dependable) to merit being summarized in a law , a verbal or mathematical description of a phenomenon that allows for general predictions. A law simply says what happens; it does not address the question of why.

One example of a law, the Law of Definite Proportions , which was discovered by the French scientist Joseph Proust (1754–1826), states that a chemical substance always contains the same proportions of elements by mass. Thus sodium chloride (table salt) always contains the same proportion by mass of sodium to chlorine, in this case 39.34% sodium and 60.66% chlorine by mass, and sucrose (table sugar) is always 42.11% carbon, 6.48% hydrogen, and 51.41% oxygen by mass. Some solid compounds do not strictly obey the law of definite proportions. The law of definite proportions should seem obvious—we would expect the composition of sodium chloride to be consistent—but the head of the US Patent Office did not accept it as a fact until the early 20th century.

Whereas a law states only what happens, a theory attempts to explain why nature behaves as it does. Laws are unlikely to change greatly over time unless a major experimental error is discovered. In contrast, a theory, by definition, is incomplete and imperfect, evolving with time to explain new facts as they are discovered. The theory developed to explain the extinction of the dinosaurs, for example, is that Earth occasionally encounters small- to medium-sized asteroids, and these encounters may have unfortunate implications for the continued existence of most species. This theory is by no means proven, but it is consistent with the bulk of evidence amassed to date. Figure \(\PageIndex{2}\) summarizes the application of the scientific method in this case.

Example \(\PageIndex{1}\)

Classify each statement as a law, a theory, an experiment, a hypothesis, a qualitative observation, or a quantitative observation.

• Ice always floats on liquid water.
• Birds evolved from dinosaurs.
• Hot air is less dense than cold air, probably because the components of hot air are moving more rapidly.
• When 10 g of ice were added to 100 mL of water at 25 °C, the temperature of the water decreased to 15.5 °C after the ice melted.
• The ingredients of Ivory soap were analyzed to see whether it really is 99.44% pure, as advertised.

Given : components of the scientific method

Asked for : statement classification

Strategy: Refer to the definitions in this section to determine which category best describes each statement.

• This is a general statement of a relationship between the properties of liquid and solid water, so it is a law.
• This is a possible explanation for the origin of birds, so it is a hypothesis.
• This is a statement that tries to explain the relationship between the temperature and the density of air based on fundamental principles, so it is a theory.
• The temperature is measured before and after a change is made in a system, so these are quantitative observations.
• This is an analysis designed to test a hypothesis (in this case, the manufacturer’s claim of purity), so it is an experiment.

Exercise \(\PageIndex{1}\)

• Measured amounts of acid were added to a Rolaids tablet to see whether it really “consumes 47 times its weight in excess stomach acid.”
• Heat always flows from hot objects to cooler ones, not in the opposite direction.
• The universe was formed by a massive explosion that propelled matter into a vacuum.
• Michael Jordan is the greatest pure shooter ever to play professional basketball.
• Limestone is relatively insoluble in water but dissolves readily in dilute acid with the evolution of a gas.
• Gas mixtures that contain more than 4% hydrogen in air are potentially explosive.

qualitative observation

quantitative observation

Because scientists can enter the cycle shown in Figure \(\PageIndex{1}\) at any point, the actual application of the scientific method to different topics can take many different forms. For example, a scientist may start with a hypothesis formed by reading about work done by others in the field, rather than by making direct observations.

It is important to remember that scientists have a tendency to formulate hypotheses in familiar terms simply because it is difficult to propose something that has never been encountered or imagined before. As a result, scientists sometimes discount or overlook unexpected findings that disagree with the basic assumptions behind the hypothesis or theory being tested. Fortunately, truly important findings are immediately subject to independent verification by scientists in other laboratories, so science is a self-correcting discipline. When the Alvarezes originally suggested that an extraterrestrial impact caused the extinction of the dinosaurs, the response was almost universal skepticism and scorn. In only 20 years, however, the persuasive nature of the evidence overcame the skepticism of many scientists, and their initial hypothesis has now evolved into a theory that has revolutionized paleontology and geology.

Chemists expand their knowledge by making observations, carrying out experiments, and testing hypotheses to develop laws to summarize their results and theories to explain them. In doing so, they are using the scientific method.

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Scientific Thinking and Critical Thinking in Science Education 

Two Distinct but Symbiotically Related Intellectual Processes

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• Published: 05 September 2023

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• Antonio García-Carmona   ORCID: orcid.org/0000-0001-5952-0340 1  

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Scientific thinking and critical thinking are two intellectual processes that are considered keys in the basic and comprehensive education of citizens. For this reason, their development is also contemplated as among the main objectives of science education. However, in the literature about the two types of thinking in the context of science education, there are quite frequent allusions to one or the other indistinctly to refer to the same cognitive and metacognitive skills, usually leaving unclear what are their differences and what are their common aspects. The present work therefore was aimed at elucidating what the differences and relationships between these two types of thinking are. The conclusion reached was that, while they differ in regard to the purposes of their application and some skills or processes, they also share others and are related symbiotically in a metaphorical sense; i.e., each one makes sense or develops appropriately when it is nourished or enriched by the other. Finally, an orientative proposal is presented for an integrated development of the two types of thinking in science classes.

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Philosophical Inquiry and Critical Thinking in Primary and Secondary Science Education

Fostering scientific literacy and critical thinking in elementary science education.

Rui Marques Vieira & Celina Tenreiro-Vieira

Enhancing Scientific Thinking Through the Development of Critical Thinking in Higher Education

Avoid common mistakes on your manuscript.

Education is not the learning of facts, but the training of the mind to think. Albert Einstein

1 Introduction

In consulting technical reports, theoretical frameworks, research, and curricular reforms related to science education, one commonly finds appeals to scientific thinking and critical thinking as essential educational processes or objectives. This is confirmed in some studies that include exhaustive reviews of the literature in this regard such as those of Bailin ( 2002 ), Costa et al. ( 2020 ), and Santos ( 2017 ) on critical thinking, and of Klarh et al. ( 2019 ) and Lehrer and Schauble ( 2006 ) on scientific thinking. However, conceptualizing and differentiating between both types of thinking based on the above-mentioned documents of science education are generally difficult. In many cases, they are referred to without defining them, or they are used interchangeably to represent virtually the same thing. Thus, for example, the document A Framework for K-12 Science Education points out that “Critical thinking is required, whether in developing and refining an idea (an explanation or design) or in conducting an investigation” (National Research Council (NRC), 2012 , p. 46). The same document also refers to scientific thinking when it suggests that basic scientific education should “provide students with opportunities for a range of scientific activities and scientific thinking , including, but not limited to inquiry and investigation, collection and analysis of evidence, logical reasoning, and communication and application of information” (NRC, 2012 , p. 251).

A few years earlier, the report Science Teaching in Schools in Europe: Policies and Research (European Commission/Eurydice, 2006 ) included the dimension “scientific thinking” as part of standardized national science tests in European countries. This dimension consisted of three basic abilities: (i) to solve problems formulated in theoretical terms , (ii) to frame a problem in scientific terms , and (iii) to formulate scientific hypotheses . In contrast, critical thinking was not even mentioned in such a report. However, in subsequent similar reports by the European Commission/Eurydice ( 2011 , 2022 ), there are some references to the fact that the development of critical thinking should be a basic objective of science teaching, although these reports do not define it at any point.

The ENCIENDE report on early-year science education in Spain also includes an explicit allusion to critical thinking among its recommendations: “Providing students with learning tools means helping them to develop critical thinking , to form their own opinions, to distinguish between knowledge founded on the evidence available at a certain moment (evidence which can change) and unfounded beliefs” (Confederation of Scientific Societies in Spain (COSCE), 2011 , p. 62). However, the report makes no explicit mention to scientific thinking. More recently, the document “ Enseñando ciencia con ciencia ” (Teaching science with science) (Couso et al., 2020 ), sponsored by Spain’s Ministry of Education, also addresses critical thinking:

(…) with the teaching approach through guided inquiry students learn scientific content, learn to do science (procedures), learn what science is and how it is built, and this (...) helps to develop critical thinking , that is, to question any statement that is not supported by evidence. (Couso et al., 2020 , p. 54)

On the other hand, in referring to what is practically the same thing, the European report Science Education for Responsible Citizenship speaks of scientific thinking when it establishes that one of the challenges of scientific education should be: “To promote a culture of scientific thinking and inspire citizens to use evidence-based reasoning for decision making” (European Commission, 2015 , p. 14). However, the Pisa 2024 Strategic Vision and Direction for Science report does not mention scientific thinking but does mention critical thinking in noting that “More generally, (students) should be able to recognize the limitations of scientific inquiry and apply critical thinking when engaging with its results” (Organization for Economic Co-operation and Development (OECD), 2020 , p. 9).

The new Spanish science curriculum for basic education (Royal Decree 217/ 2022 ) does make explicit reference to scientific thinking. For example, one of the STEM (Science, Technology, Engineering, and Mathematics) competency descriptors for compulsory secondary education reads:

Use scientific thinking to understand and explain the phenomena that occur around them, trusting in knowledge as a motor for development, asking questions and checking hypotheses through experimentation and inquiry (...) showing a critical attitude about the scope and limitations of science. (p. 41,599)

Furthermore, when developing the curriculum for the subjects of physics and chemistry, the same provision clarifies that “The essence of scientific thinking is to understand what are the reasons for the phenomena that occur in the natural environment to then try to explain them through the appropriate laws of physics and chemistry” (Royal Decree 217/ 2022 , p. 41,659). However, within the science subjects (i.e., Biology and Geology, and Physics and Chemistry), critical thinking is not mentioned as such. Footnote 1 It is only more or less directly alluded to with such expressions as “critical analysis”, “critical assessment”, “critical reflection”, “critical attitude”, and “critical spirit”, with no attempt to conceptualize it as is done with regard to scientific thinking.

The above is just a small sample of the concepts of scientific thinking and critical thinking only being differentiated in some cases, while in others they are presented as interchangeable, using one or the other indistinctly to talk about the same cognitive/metacognitive processes or practices. In fairness, however, it has to be acknowledged—as said at the beginning—that it is far from easy to conceptualize these two types of thinking (Bailin, 2002 ; Dwyer et al., 2014 ; Ennis, 2018 ; Lehrer & Schauble, 2006 ; Kuhn, 1993 , 1999 ) since they feed back on each other, partially overlap, and share certain features (Cáceres et al., 2020 ; Vázquez-Alonso & Manassero-Mas, 2018 ). Neither is there unanimity in the literature on how to characterize each of them, and rarely have they been analyzed comparatively (e.g., Hyytinen et al., 2019 ). For these reasons, I believed it necessary to address this issue with the present work in order to offer some guidelines for science teachers interested in deepening into these two intellectual processes to promote them in their classes.

2 An Attempt to Delimit Scientific Thinking in Science Education

For many years, cognitive science has been interested in studying what scientific thinking is and how it can be taught in order to improve students’ science learning (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ). To this end, Kuhn et al. propose taking a characterization of science as argument (Kuhn, 1993 ; Kuhn et al., 2008 ). They argue that this is a suitable way of linking the activity of how scientists think with that of the students and of the public in general, since science is a social activity which is subject to ongoing debate, in which the construction of arguments plays a key role. Lehrer and Schauble ( 2006 ) link scientific thinking with scientific literacy, paying especial attention to the different images of science. According to those authors, these images would guide the development of the said literacy in class. The images of science that Leherer and Schauble highlight as characterizing scientific thinking are: (i) science-as-logical reasoning (role of domain-general forms of scientific reasoning, including formal logic, heuristic, and strategies applied in different fields of science), (ii) science-as-theory change (science is subject to permanent revision and change), and (iii) science-as-practice (scientific knowledge and reasoning are components of a larger set of activities that include rules of participation, procedural skills, epistemological knowledge, etc.).

Based on a literature review, Jirout ( 2020 ) defines scientific thinking as an intellectual process whose purpose is the intentional search for information about a phenomenon or facts by formulating questions, checking hypotheses, carrying out observations, recognizing patterns, and making inferences (a detailed description of all these scientific practices or competencies can be found, for example, in NRC, 2012 ; OECD, 2019 ). Therefore, for Jirout, the development of scientific thinking would involve bringing into play the basic science skills/practices common to the inquiry-based approach to learning science (García-Carmona, 2020 ; Harlen, 2014 ). For other authors, scientific thinking would include a whole spectrum of scientific reasoning competencies (Krell et al., 2022 ; Moore, 2019 ; Tytler & Peterson, 2004 ). However, these competences usually cover the same science skills/practices mentioned above. Indeed, a conceptual overlap between scientific thinking, scientific reasoning, and scientific inquiry is often found in science education goals (Krell et al., 2022 ). Although, according to Leherer and Schauble ( 2006 ), scientific thinking is a broader construct that encompasses the other two.

It could be said that scientific thinking is a particular way of searching for information using science practices Footnote 2 (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ; Vázquez-Alonso & Manassero-Mas, 2018 ). This intellectual process provides the individual with the ability to evaluate the robustness of evidence for or against a certain idea, in order to explain a phenomenon (Clouse, 2017 ). But the development of scientific thinking also requires metacognition processes. According to what Kuhn ( 2022 ) argues, metacognition is fundamental to the permanent control or revision of what an individual thinks and knows, as well as that of the other individuals with whom it interacts, when engaging in scientific practices. In short, scientific thinking demands a good connection between reasoning and metacognition (Kuhn, 2022 ). Footnote 3

From that perspective, Zimmerman and Klarh ( 2018 ) have synthesized a taxonomy categorizing scientific thinking, relating cognitive processes with the corresponding science practices (Table 1 ). It has to be noted that this taxonomy was prepared in line with the categorization of scientific practices proposed in the document A Framework for K-12 Science Education (NRC, 2012 ). This is why one needs to understand that, for example, the cognitive process of elaboration and refinement of hypotheses is not explicitly associated with the scientific practice of hypothesizing but only with the formulation of questions. Indeed, the K-12 Framework document does not establish hypothesis formulation as a basic scientific practice. Lederman et al. ( 2014 ) justify it by arguing that not all scientific research necessarily allows or requires the verification of hypotheses, for example, in cases of exploratory or descriptive research. However, the aforementioned document (NRC, 2012 , p. 50) does refer to hypotheses when describing the practice of developing and using models , appealing to the fact that they facilitate the testing of hypothetical explanations .

In the literature, there are also other interesting taxonomies characterizing scientific thinking for educational purposes. One of them is that of Vázquez-Alonso and Manassero-Mas ( 2018 ) who, instead of science practices, refer to skills associated with scientific thinking . Their characterization basically consists of breaking down into greater detail the content of those science practices that would be related to the different cognitive and metacognitive processes of scientific thinking. Also, unlike Zimmerman and Klarh’s ( 2018 ) proposal, Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal explicitly mentions metacognition as one of the aspects of scientific thinking, which they call meta-process . In my opinion, the proposal of the latter authors, which shells out scientific thinking into a broader range of skills/practices, can be more conducive in order to favor its approach in science classes, as teachers would have more options to choose from to address components of this intellectual process depending on their teaching interests, the educational needs of their students and/or the learning objectives pursued. Table 2 presents an adapted characterization of the Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal to address scientific thinking in science education.

3 Contextualization of Critical Thinking in Science Education

Theorization and research about critical thinking also has a long tradition in the field of the psychology of learning (Ennis, 2018 ; Kuhn, 1999 ), and its application extends far beyond science education (Dwyer et al., 2014 ). Indeed, the development of critical thinking is commonly accepted as being an essential goal of people’s overall education (Ennis, 2018 ; Hitchcock, 2017 ; Kuhn, 1999 ; Willingham, 2008 ). However, its conceptualization is not simple and there is no unanimous position taken on it in the literature (Costa et al., 2020 ; Dwyer et al., 2014 ); especially when trying to relate it to scientific thinking. Thus, while Tena-Sánchez and León-Medina ( 2022 ) Footnote 4 and McBain et al. ( 2020 ) consider critical thinking to be the basis of or forms part of scientific thinking, Dowd et al. ( 2018 ) understand scientific thinking to be just a subset of critical thinking. However, Vázquez-Alonso and Manassero-Mas ( 2018 ) do not seek to determine whether critical thinking encompasses scientific thinking or vice versa. They consider that both types of knowledge share numerous skills/practices and the progressive development of one fosters the development of the other as a virtuous circle of improvement. Other authors, such as Schafersman ( 1991 ), even go so far as to say that critical thinking and scientific thinking are the same thing. In addition, some views on the relationship between critical thinking and scientific thinking seem to be context-dependent. For example, Hyytine et al. ( 2019 ) point out that in the perspective of scientific thinking as a component of critical thinking, the former is often used to designate evidence-based thinking in the sciences, although this view tends to dominate in Europe but not in the USA context. Perhaps because of this lack of consensus, the two types of thinking are often confused, overlapping, or conceived as interchangeable in education.

Even with such a lack of unanimous or consensus vision, there are some interesting theoretical frameworks and definitions for the development of critical thinking in education. One of the most popular definitions of critical thinking is that proposed by The National Council for Excellence in Critical Thinking (1987, cited in Inter-American Teacher Education Network, 2015 , p. 6). This conceives of it as “the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action”. In other words, critical thinking can be regarded as a reflective and reasonable class of thinking that provides people with the ability to evaluate multiple statements or positions that are defensible to then decide which is the most defensible (Clouse, 2017 ; Ennis, 2018 ). It thus requires, in addition to a basic scientific competency, notions about epistemology (Kuhn, 1999 ) to understand how knowledge is constructed. Similarly, it requires skills for metacognition (Hyytine et al., 2019 ; Kuhn, 1999 ; Magno, 2010 ) since critical thinking “entails awareness of one’s own thinking and reflection on the thinking of self and others as objects of cognition” (Dean & Kuhn, 2003 , p. 3).

In science education, one of the most suitable scenarios or resources, but not the only one, Footnote 5 to address all these aspects of critical thinking is through the analysis of socioscientific issues (SSI) (Taylor et al., 2006 ; Zeidler & Nichols, 2009 ). Without wishing to expand on this here, I will only say that interesting works can be found in the literature that have analyzed how the discussion of SSIs can favor the development of critical thinking skills (see, e.g., López-Fernández et al., 2022 ; Solbes et al., 2018 ). For example, López-Fernández et al. ( 2022 ) focused their teaching-learning sequence on the following critical thinking skills: information analysis, argumentation, decision making, and communication of decisions. Even some authors add the nature of science (NOS) to this framework (i.e., SSI-NOS-critical thinking), as, for example, Yacoubian and Khishfe ( 2018 ) in order to develop critical thinking and how this can also favor the understanding of NOS (Yacoubian, 2020 ). In effect, as I argued in another work on the COVID-19 pandemic as an SSI, in which special emphasis was placed on critical thinking, an informed understanding of how science works would have helped the public understand why scientists were changing their criteria to face the pandemic in the light of new data and its reinterpretations, or that it was not possible to go faster to get an effective and secure medical treatment for the disease (García-Carmona, 2021b ).

In the recent literature, there have also been some proposals intended to characterize critical thinking in the context of science education. Table 3 presents two of these by way of example. As can be seen, both proposals share various components for the development of critical thinking (respect for evidence, critically analyzing/assessing the validity/reliability of information, adoption of independent opinions/decisions, participation, etc.), but that of Blanco et al. ( 2017 ) is more clearly contextualized in science education. Likewise, that of these authors includes some more aspects (or at least does so more explicitly), such as developing epistemological Footnote 6 knowledge of science (vision of science…) and on its interactions with technology, society, and environment (STSA relationships), and communication skills. Therefore, it offers a wider range of options for choosing critical thinking skills/processes to promote it in science classes. However, neither proposal refers to metacognitive skills, which are also essential for developing critical thinking (Kuhn, 1999 ).

3.1 Critical thinking vs. scientific thinking in science education: differences and similarities

In accordance with the above, it could be said that scientific thinking is nourished by critical thinking, especially when deciding between several possible interpretations and explanations of the same phenomenon since this generally takes place in a context of debate in the scientific community (Acevedo-Díaz & García-Carmona, 2017 ). Thus, the scientific attitude that is perhaps most clearly linked to critical thinking is the skepticism with which scientists tend to welcome new ideas (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ), especially if they are contrary to well-established scientific knowledge (Bell, 2009 ). A good example of this was the OPERA experiment (García-Carmona & Acevedo-Díaz, 2016a ), which initially seemed to find that neutrinos could move faster than the speed of light. This finding was supposed to invalidate Albert Einstein’s theory of relativity (the finding was later proved wrong). In response, Nobel laureate in physics Sheldon L. Glashow went so far as to state that:

the result obtained by the OPERA collaboration cannot be correct. If it were, we would have to give up so many things, it would be such a huge sacrifice... But if it is, I am officially announcing it: I will shout to Mother Nature: I’m giving up! And I will give up Physics. (BBVA Foundation, 2011 )

Indeed, scientific thinking is ultimately focused on getting evidence that may support an idea or explanation about a phenomenon, and consequently allow others that are less convincing or precise to be discarded. Therefore when, with the evidence available, science has more than one equally defensible position with respect to a problem, the investigation is considered inconclusive (Clouse, 2017 ). In certain cases, this gives rise to scientific controversies (Acevedo-Díaz & García-Carmona, 2017 ) which are not always resolved based exclusively on epistemic or rational factors (Elliott & McKaughan, 2014 ; Vallverdú, 2005 ). Hence, it is also necessary to integrate non-epistemic practices into the framework of scientific thinking (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ), practices that transcend the purely rational or cognitive processes, including, for example, those related to emotional or affective issues (Sinatra & Hofer, 2021 ). From an educational point of view, this suggests that for students to become more authentically immersed in the way of working or thinking scientifically, they should also learn to feel as scientists do when they carry out their work (Davidson et al., 2020 ). Davidson et al. ( 2020 ) call it epistemic affect , and they suggest that it could be approach in science classes by teaching students to manage their frustrations when they fail to achieve the expected results; Footnote 7 or, for example, to moderate their enthusiasm with favorable results in a scientific inquiry by activating a certain skepticism that encourages them to do more testing. And, as mentioned above, for some authors, having a skeptical attitude is one of the actions that best visualize the application of critical thinking in the framework of scientific thinking (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ).

On the other hand, critical thinking also draws on many of the skills or practices of scientific thinking, as discussed above. However, in contrast to scientific thinking, the coexistence of two or more defensible ideas is not, in principle, a problem for critical thinking since its purpose is not so much to invalidate some ideas or explanations with respect to others, but rather to provide the individual with the foundations on which to position themself with the idea/argument they find most defensible among several that are possible (Ennis, 2018 ). For example, science with its methods has managed to explain the greenhouse effect, the phenomenon of the tides, or the transmission mechanism of the coronavirus. For this, it had to discard other possible explanations as they were less valid in the investigations carried out. These are therefore issues resolved by the scientific community which create hardly any discussion at the present time. However, taking a position for or against the production of energy in nuclear power plants transcends the scope of scientific thinking since both positions are, in principle, equally defensible. Indeed, within the scientific community itself there are supporters and detractors of the two positions, based on the same scientific knowledge. Consequently, it is critical thinking, which requires the management of knowledge and scientific skills, a basic understanding of epistemic (rational or cognitive) and non-epistemic (social, ethical/moral, economic, psychological, cultural, ...) aspects of the nature of science, as well as metacognitive skills, which helps the individual forge a personal foundation on which to position themself in one place or another, or maintain an uncertain, undecided opinion.

In view of the above, one can summarize that scientific thinking and critical thinking are two different intellectual processes in terms of purpose, but are related symbiotically (i.e., one would make no sense without the other or both feed on each other) and that, in their performance, they share a fair number of features, actions, or mental skills. According to Cáceres et al. ( 2020 ) and Hyytine et al. ( 2019 ), the intellectual skills that are most clearly common to both types of thinking would be searching for relationships between evidence and explanations , as well as investigating and logical thinking to make inferences . To this common space, I would also add skills for metacognition in accordance with what has been discussed about both types of knowledge (Khun, 1999 , 2022 ).

In order to compile in a compact way all that has been argued so far, in Table 4 , I present my overview of the relationship between scientific thinking and critical thinking. I would like to point out that I do not intend to be extremely extensive in the compilation, in the sense that possibly more elements could be added in the different sections, but rather to represent above all the aspects that distinguish and share them, as well as the mutual enrichment (or symbiosis) between them.

4 A Proposal for the Integrated Development of Critical Thinking and Scientific Thinking in Science Classes

Once the differences, common aspects, and relationships between critical thinking and scientific thinking have been discussed, it would be relevant to establish some type of specific proposal to foster them in science classes. Table 5 includes a possible script to address various skills or processes of both types of thinking in an integrated manner. However, before giving guidance on how such skills/processes could be approached, I would like to clarify that while all of them could be dealt within the context of a single school activity, I will not do so in this way. First, because I think that it can give the impression that the proposal is only valid if it is applied all at once in a specific learning situation, which can also discourage science teachers from implementing it in class due to lack of time or training to do so. Second, I think it can be more interesting to conceive the proposal as a set of thinking skills or actions that can be dealt with throughout the different science contents, selecting only (if so decided) some of them, according to educational needs or characteristics of the learning situation posed in each case. Therefore, in the orientations for each point of the script or grouping of these, I will use different examples and/or contexts. Likewise, these orientations in the form of comments, although founded in the literature, should be considered only as possibilities to do so, among many others possible.

Motivation and predisposition to reflect and discuss (point i ) demands, on the one hand, that issues are chosen which are attractive for the students. This can be achieved, for example, by asking the students directly what current issues, related to science and its impact or repercussions, they would like to learn about, and then decide on which issue to focus on (García-Carmona, 2008 ). Or the teacher puts forward the issue directly in class, trying for it be current, to be present in the media, social networks, etc., or what they think may be of interest to their students based on their teaching experience. In this way, each student is encouraged to feel questioned or concerned as a citizen because of the issue that is going to be addressed (García-Carmona, 2008 ). Also of possible interest is the analysis of contemporary, as yet unresolved socioscientific affairs (Solbes et al., 2018 ), such as climate change, science and social justice, transgenic foods, homeopathy, and alcohol and drug use in society. But also, everyday questions can be investigated which demand a decision to be made, such as “What car to buy?” (Moreno-Fontiveros et al., 2022 ), or “How can we prevent the arrival of another pandemic?” (Ushola & Puig, 2023 ).

On the other hand, it is essential that the discussion about the chosen issue is planned through an instructional process that generates an environment conducive to reflection and debate, with a view to engaging the students’ participation in it. This can be achieved, for example, by setting up a role-play game (Blanco-López et al., 2017 ), especially if the issue is socioscientific, or by critical and reflective reading of advertisements with scientific content (Campanario et al., 2001 ) or of science-related news in the daily media (García-Carmona, 2014 , 2021a ; Guerrero-Márquez & García-Carmona, 2020 ; Oliveras et al., 2013 ), etc., for subsequent discussion—all this, in a collaborative learning setting and with a clear democratic spirit.

Respect for scientific evidence (point ii ) should be the indispensable condition in any analysis and discussion from the prisms of scientific and of critical thinking (Erduran, 2021 ). Although scientific knowledge may be impregnated with subjectivity during its construction and is revisable in the light of new evidence ( tentativeness of scientific knowledge), when it is accepted by the scientific community it is as objective as possible (García-Carmona & Acevedo-Díaz, 2016b ). Therefore, promoting trust and respect for scientific evidence should be one of the primary educational challenges to combating pseudoscientists and science deniers (Díaz & Cabrera, 2022 ), whose arguments are based on false beliefs and assumptions, anecdotes, and conspiracy theories (Normand, 2008 ). Nevertheless, it is no simple task to achieve the promotion or respect for scientific evidence (Fackler, 2021 ) since science deniers, for example, consider that science is unreliable because it is imperfect (McIntyre, 2021 ). Hence the need to promote a basic understanding of NOS (point iii ) as a fundamental pillar for the development of both scientific thinking and critical thinking. A good way to do this would be through explicit and reflective discussion about controversies from the history of science (Acevedo-Díaz & García-Carmona, 2017 ) or contemporary controversies (García-Carmona, 2021b ; García-Carmona & Acevedo-Díaz, 2016a ).

Also, with respect to point iii of the proposal, it is necessary to manage basic scientific knowledge in the development of scientific and critical thinking skills (Willingham, 2008 ). Without this, it will be impossible to develop a minimally serious and convincing argument on the issue being analyzed. For example, if one does not know the transmission mechanism of a certain disease, it is likely to be very difficult to understand or justify certain patterns of social behavior when faced with it. In general, possessing appropriate scientific knowledge on the issue in question helps to make the best interpretation of the data and evidence available on this issue (OECD, 2019 ).

The search for information from reliable sources, together with its analysis and interpretation (points iv to vi ), are essential practices both in purely scientific contexts (e.g., learning about the behavior of a given physical phenomenon from literature or through enquiry) and in the application of critical thinking (e.g., when one wishes to take a personal, but informed, position on a particular socio-scientific issue). With regard to determining the credibility of information with scientific content on the Internet, Osborne et al. ( 2022 ) propose, among other strategies, to check whether the source is free of conflicts of interest, i.e., whether or not it is biased by ideological, political or economic motives. Also, it should be checked whether the source and the author(s) of the information are sufficiently reputable.

Regarding the interpretation of data and evidence, several studies have shown the difficulties that students often have with this practice in the context of enquiry activities (e.g., Gobert et al., 2018 ; Kanari & Millar, 2004 ; Pols et al., 2021 ), or when analyzing science news in the press (Norris et al., 2003 ). It is also found that they have significant difficulties in choosing the most appropriate data to support their arguments in causal analyses (Kuhn & Modrek, 2022 ). However, it must be recognized that making interpretations or inferences from data is not a simple task; among other reasons, because their construction is influenced by multiple factors, both epistemic (prior knowledge, experimental designs, etc.) and non-epistemic (personal expectations, ideology, sociopolitical context, etc.), which means that such interpretations are not always the same for all scientists (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ). For this reason, the performance of this scientific practice constitutes one of the phases or processes that generate the most debate or discussion in a scientific community, as long as no consensus is reached. In order to improve the practice of making inferences among students, Kuhn and Lerman ( 2021 ) propose activities that help them develop their own epistemological norms to connect causally their statements with the available evidence.

Point vii refers, on the one hand, to an essential scientific practice: the elaboration of evidence-based scientific explanations which generally, in a reasoned way, account for the causality, properties, and/or behavior of the phenomena (Brigandt, 2016 ). In addition, point vii concerns the practice of argumentation . Unlike scientific explanations, argumentation tries to justify an idea, explanation, or position with the clear purpose of persuading those who defend other different ones (Osborne & Patterson, 2011 ). As noted above, the complexity of most socioscientific issues implies that they have no unique valid solution or response. Therefore, the content of the arguments used to defend one position or another are not always based solely on purely rational factors such as data and scientific evidence. Some authors defend the need to also deal with non-epistemic aspects of the nature of science when teaching it (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ) since many scientific and socioscientific controversies are resolved by different factors or go beyond just the epistemic (Vallverdú, 2005 ).

To defend an idea or position taken on an issue, it is not enough to have scientific evidence that supports it. It is also essential to have skills for the communication and discussion of ideas (point viii ). The history of science shows how the difficulties some scientists had in communicating their ideas scientifically led to those ideas not being accepted at the time. A good example for students to become aware of this is the historical case of Semmelweis and puerperal fever (Aragón-Méndez et al., 2019 ). Its reflective reading makes it possible to conclude that the proposal of this doctor that gynecologists disinfect their hands, when passing from one parturient to another to avoid contagions that provoked the fever, was rejected by the medical community not only for epistemic reasons, but also for the difficulties that he had to communicate his idea. The history of science also reveals that some scientific interpretations were imposed on others at certain historical moments due to the rhetorical skills of their proponents although none of the explanations would convincingly explain the phenomenon studied. An example is the case of the controversy between Pasteur and Liebig about the phenomenon of fermentation (García-Carmona & Acevedo-Díaz, 2017 ), whose reading and discussion in science class would also be recommended in this context of this critical and scientific thinking skill. With the COVID-19 pandemic, for example, the arguments of some charlatans in the media and on social networks managed to gain a certain influence in the population, even though scientifically they were muddled nonsense (García-Carmona, 2021b ). Therefore, the reflective reading of news on current SSIs such as this also constitutes a good resource for the same educational purpose. In general, according to Spektor-Levy et al. ( 2009 ), scientific communication skills should be addressed explicitly in class, in a progressive and continuous manner, including tasks of information seeking, reading, scientific writing, representation of information, and representation of the knowledge acquired.

Finally (point ix ), a good scientific/critical thinker must be aware of what they know, of what they have doubts about or do not know, to this end continuously practicing metacognitive exercises (Dean & Kuhn, 2003 ; Hyytine et al., 2019 ; Magno, 2010 ; Willingham, 2008 ). At the same time, they must recognize the weaknesses and strengths of the arguments of their peers in the debate in order to be self-critical if necessary, as well as to revising their own ideas and arguments to improve and reorient them, etc. ( self-regulation ). I see one of the keys of both scientific and critical thinking being the capacity or willingness to change one’s mind, without it being frowned upon. Indeed, quite the opposite since one assumes it to occur thanks to the arguments being enriched and more solidly founded. In other words, scientific and critical thinking and arrogance or haughtiness towards the rectification of ideas or opinions do not stick well together.

5 Final Remarks

For decades, scientific thinking and critical thinking have received particular attention from different disciplines such as psychology, philosophy, pedagogy, and specific areas of this last such as science education. The two types of knowledge represent intellectual processes whose development in students, and in society in general, is considered indispensable for the exercise of responsible citizenship in accord with the demands of today’s society (European Commission, 2006 , 2015 ; NRC, 2012 ; OECD, 2020 ). As has been shown however, the task of their conceptualization is complex, and teaching students to think scientifically and critically is a difficult educational challenge (Willingham, 2008 ).

Aware of this, and after many years dedicated to science education, I felt the need to organize my ideas regarding the aforementioned two types of thinking. In consulting the literature about these, I found that, in many publications, scientific thinking and critical thinking are presented or perceived as being interchangeable or indistinguishable; a conclusion also shared by Hyytine et al. ( 2019 ). Rarely have their differences, relationships, or common features been explicitly studied. So, I considered that it was a matter needing to be addressed because, in science education, the development of scientific thinking is an inherent objective, but, when critical thinking is added to the learning objectives, there arise more than reasonable doubts about when one or the other would be used, or both at the same time. The present work came about motivated by this, with the intention of making a particular contribution, but based on the relevant literature, to advance in the question raised. This converges in conceiving scientific thinking and critical thinking as two intellectual processes that overlap and feed into each other in many aspects but are different with respect to certain cognitive skills and in terms of their purpose. Thus, in the case of scientific thinking, the aim is to choose the best possible explanation of a phenomenon based on the available evidence, and it therefore involves the rejection of alternative explanatory proposals that are shown to be less coherent or convincing. Whereas, from the perspective of critical thinking, the purpose is to choose the most defensible idea/option among others that are also defensible, using both scientific and extra-scientific (i.e., moral, ethical, political, etc.) arguments. With this in mind, I have described a proposal to guide their development in the classroom, integrating them under a conception that I have called, metaphorically, a symbiotic relationship between two modes of thinking.

Critical thinking is mentioned literally in other of the curricular provisions’ subjects such as in Education in Civics and Ethical Values or in Geography and History (Royal Decree 217/2022).

García-Carmona ( 2021a ) conceives of them as activities that require the comprehensive application of procedural skills, cognitive and metacognitive processes, and both scientific knowledge and knowledge of the nature of scientific practice .

Kuhn ( 2021 ) argues that the relationship between scientific reasoning and metacognition is especially fostered by what she calls inhibitory control , which basically consists of breaking down the whole of a thought into parts in such a way that attention is inhibited on some of those parts to allow a focused examination of the intended mental content.

Specifically, Tena-Sánchez and León-Medina (2020) assume that critical thinking is at the basis of rational or scientific skepticism that leads to questioning any claim that does not have empirical support.

As discussed in the introduction, the inquiry-based approach is also considered conducive to addressing critical thinking in science education (Couso et al., 2020 ; NRC, 2012 ).

Epistemic skills should not be confused with epistemological knowledge (García-Carmona, 2021a ). The former refers to skills to construct, evaluate, and use knowledge, and the latter to understanding about the origin, nature, scope, and limits of scientific knowledge.

For this purpose, it can be very useful to address in class, with the help of the history and philosophy of science, that scientists get more wrong than right in their research, and that error is always an opportunity to learn (García-Carmona & Acevedo-Díaz, 2018 ).

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Problem Solving (Pemecahan Masalah) : Pengertian, Indikator, Faktor, dsb

Salah satu keterampilan yang digaungkan untuk menghadapi era pendidikan abad 21 adalah problem solving atau pemecahan masalah. Pemecahan masalah merupakan salah satu skill set penting untuk menghadapi tuntutan hidup di zaman yang serba cepat ini. Mengapa? Karena kecepatan dan ketelitian merupakan hal yang amat berbenturan, dan ketika kita ingin mewujudkannya, maka akan timbul banyak permasalahan, yakni kesenjangan antara harapan dan kenyataan. Dengan demikian keterampilan problem solving amatlah dibutuhkan di masa ini.

Namun demikian tidak usah menyalahkan kebutuhan abad 21, revolusi industri 4.0, atau pengaruh globalisasi juga pada dasarnya setiap orang akan menghadapi masalah. Kita semua akan selalu menemui masalah dalam kehidupan sehari-hari dan akan selalu berusaha untuk memecahkannya. Tentunya tingkat kesulitannya amatlah beragam, mulai dari yang sudah memiliki langkah untuk menyelesaikannya, hingga masalah baru yang lebih sulit untuk dipecahkan.

Oleh karena itu problem solving serta kemampuan memecahkan masalah merupakan konsep dan keterampilan penting yang harus dipahami dan dikuasai. Berikut adalah berbagai uraian mengenai problem solving atau pemecahan masalah mulai dari pengertian, indikator, hingga faktor-faktor yang memengaruhinya.

Pengertian Problem Solving

Menurut Uno (2014, hlm. 134) problem solving adalah kemampuan untuk menggunakan proses berpikir dalam memecahkan masalah dengan mengumpulkan fakta, menganalisis informasi, penyusunan alternatif solusi, serta memilih solusi masalah yang lebih efektif. Artinya problem solving merupakan pencarian solusi melalui proses berpikir yang sistematis.

Sementara itu menurut Lucenario dkk (dalam Khoiriyah & Husana, 2018, hlm. 151) problem solving adalah aktivitas yang membutuhkan seseorang antuk memilih jalan keluar yang dapat dilakukan berdasarkan kemampuan yang dimilikinya yang berarti melakukan pergerakan antara keadaan sekarang dengan kondisi yang diharapkan. Hal ini berkaitan dengan definisi masalah yang berarti kenyataan yang tidak sesuai dengan kenyataan, dan problem solving berusaha untuk memperbaiki kenyataan tersebut menjadi sesuai dengan harapan.

Selanjutnya, menurut Solso (dalam Mawaddah, 2015) pemecahan masalah adalah suatu pemikiran yang terarah secara langsung untuk menentukan solusi atau jalan keluar untuk suatu masalah yang spesifik. Tentunya solusi spesifik berarti solusi yang sesuai dengan masalah yang terjadi. Selain itu, Gagne dalam (Made, 2016, hlm. 52) mengemukakan bahwa problem solving dapat dipandang sebagai suatu proses untuk menemukan kombinasi dari sejumlah aturan yang dapat diterapkan dalam upaya mengatasi situasi yang baru. Kombinasi dari sejumlah aturan dapat dipahami sebagai algoritma atau langkah-langkah yang dapat menyelesaikan suatu permasalahan.

Berdasarkan pendapat-pendapat ahli di atas dapat disimpulkan bahwa problem solving adalah aktivitas proses berpikir untuk mencari solusi berupa suatu prosedur atau langkah yang spesifik dalam menyelesaikan suatu permasalahan secara sistematis berdasarkan kemampuan yang dimilikinya.

Jenis Masalah

Terdapat beberapa jenis masalah, yaitu:

• Masalah yang prosedur pemecahannya sudah ada dan telah diketahui siswa;
• Masalah yang prosedur pemecahannya belum diketahui oleh siswa;
• Masalah yang sama sekali belum diketahui prosedur pemecahannya dan atau belum diketahui data yang diperlukan untuk mencari solusinya.

Tentunya dalam pendidikan abad 21, kemampuan pemecahan masalah yang diharapkan dapat dikuasai adalah penyelesaian masalah terhadap masalah yang belum diketahui prosedur pemecahannya dan atau belum diketahui data yang diperlukan untuk mencari solusinya.

Indikator Problem Solving

Bagaimana caranya kita mengetahui bahwa seseorang atau dalam bidang pendidikan spesifiknya peserta didik telah mampu menggunakan kemampuan problem solvingnya? Terdapat indikator yang dapat mencirikan bahwa seseorang tengah mempraktikan kemampuan pemecahan masalah. Menurut Johnson & Johnson (Tawil & Liliasari, 2013, hlm. 93) indikator-indikator penyelesaian masalah adalah sebagai berikut.

• “Mampu mendefinisikan masalah, yaitu merumuskan masalah dari peristiwa tertentu yang mengandung isu konflik, sehingga peserta didik mengerti masalah apa yang akan dikaji. Dalam hal ini, peserta didik harus mampu mendefinisikan beberapa masalah mengenai isu-isu hangat yang terjadi di lingkungannya;
• “Mampu mendiagnosis masalah, yaitu menentukan sebab-sebab terjadinya masalah, serta menganalisis berbagai faktor, baik faktor yang bisa menghambat maupun faktor yang dapat mendukung dalam penyelesaian masalah”. Jika hal yang pertama dilakukan adalah mengindentifikasi masalah, maka selanjutnya peserta didik harus dapat menyelidiki ataupun menemukan sebab atau alasan terjadi suatu permasalahan tersebut sehingga bisa mencari solusi;
• “Mampu merumuskan alternatif strategi, yaitu menguji setiap tindakan yang telah dirumuskan melalui diskusi kelas”. Mengatasi suatu permasalahan tentunya bisa melakukan berbagai hal sesuai tingkat permasalahan yang ada. Strategi yang dilakukan pun bisa berbedabeda sehingga perlu adanya alternatif strategi yang lain jika salah satu strategi tidak dapat berhasil mengatasi suatu permasalahan tersebut;
• “Mampu menentukan dan menerapkan strategi pilihan, yaitu pengambilan keputusan tentang strategi mana yang dapat dilakukan”. Pengambilan keputusan sangat diperlukan dalam memecahkan suatu masalah karena menentukan strategi yang paling baik dari beberapa alternatif strategi yang ada;
• “Mampu melakukan evaluasi, baik evaluasi proses maupun evaluasi hasil”. Evaluasi dilakukan agar dapat memperbaiki hal-hal yang salah dari kegiatan proses maupun hasil yang dilakukan ketika memecahkan suatu masalah. Sehingga akan menjadi cerminan untuk selanjutnya agar melakukan strategi yang lebih baik lagi.

Tabel Indikator Problem Solving

Jika disusun dalam tabel indikator seperti layaknya indikator-indikator lainnya dalam bidang pendidikan, maka indikator penyelesaian masalah dapat dijabarkan sebagai berikut.

Sumber: Tawil & Liliasari, (2013, hlm. 93)

Faktor-Faktor yang Mempengaruhi Kemampuan Problem Solving

Menurut Kartika,(2017, hlm. 327) faktor-faktor yang mempengaruhi kemampuan pemecahan masalah adalah sebagai berikut.

• Pengalaman Pengalaman terhadap tugas-tugas menyelesaikan soal wacana atau soal aplikasi. Pengalaman awal seperti ketakutan terhadap biolohi dapat menghambat kemampuan siswa dalam memecahkan masalah.
• Motivasi Dorongan yang kuat dari dalam diri seperti menumbuhkan keyakinan bahwa dirinya bisa, maupun dorongan dari luar diri (eksternal) seperti diberikan soal-soal yang menarik, menantang dapat mempengaruhi hasil pemecahan masalah.
• Kemampuan memahami masalah Kemampuan siswa terhadap konsep-konsep soal, tugas, atau permasalahan nyata yang berbeda-beda tingkatnya dapat memicu perbedaan kemampuan siswa dalam memecahkan masalah.
• Keterampilan Keterampilan adalah kemampuan untuk menggunakan akal, pikiran, ide dan kreativitas dalam mengerjakan, mengubah ataupun membuat sesuatu menjadi lebih bermakna sehingga menghasilkan sebuah nilai dari hasil pekerjaan tersebut. keterampilan tersebut pada dasarnya akan lebih baik bila terus diasah dan dilatih untuk menaikkan kemampuan sehingga akan menjadi ahli atau menguasai dari salah satu bidang keterampilan yang ada.
• Kemandirian Kemandirian adalah kemampuan seseorang untuk melakukan suatu hal apapun sendiri, tidak bergantung pada orang lain. Sikap mandiri dapat membuat seseorang mampu menghadapi masalah yang ada. Sebaliknya, seseorang yang tidak memiliki sikap mandiri, dia tidak mampu menghadapi jika ada masalah.
• Kepercayaan diri Kepercayaan diri akan memperkuat motivasi mencapai keberhasilan, karena semakin tinggi kepercayaan terhadap kemampuan diri sendiri, semakin kuat pula semangat untuk menyelesaikan pekerjaannya.

Langkah-langkah Problem Solving

Langkah-langkah yang dapat dilakukan dalam melakukan penyelesaian masalah adalah sebagai berikut.

• Memahami Masalah Langkah ini sangat menekankan kesuksesan memperoleh solusi masalah. Langkah ini melibatkan pendalaman situasi masalah, melakukan pemilahan fakta – fakta menentukan hubungan di antara fakta-fakta dan membuat formulasi pertanyaan masalah. Setiap masalah yang ditulis, bahkan yang paling mudah sekalipun harus dibaca berulang kali dan informasi yang terdapat dalam masalah dipelajari dengan seksama. Biasanya siswa harus menyatakan kembali masalah dalam bahasanya sendiri.
• Membuat Rencana Pemecahan Masalahi Langkah ini perlu dilakukan dengan percaya diri ketika masalah sudah dapat dipahami. Rencana solusi dibangun dengan mempertimbangkan struktur masalah dan pertanyaan yang harus dijawab. Jika masalah tersebut adalah masalah rutin dengan tugas menulis kalimat matematika terbuka, maka perlu dilakukan penerjemah masalah menjadi bahasa matematika. Jika masalah yang dihadapi adalah masalah nonrutin, maka suatu rencana perlu dibuat, bahkan kadang strategi baru perlu digambarkan.
• Melaksanakan Rencana Pemecahan Masalahi Untuk mencari solusi yang tepat, rencana yang sudah dibuat dalam langkah harus dilaksanakan dengan hati-hati. Untuk melalui, estimasi solusi yang dibuat sangat perlu. Diagram, tabel, atau urutan dibangun secara seksama sehingga si pemecah masalah tidak akan bingung. Tabel digunakan jika perlu. Jika solusi memerlukan komputasi, kebanyakan individu akan menggunakan kalkulator untuk menghitung daripada menghitung dengan kertas dan pensil dan mengurangi kekhawatiran yang sering terjadi dalam pemecahan masalah. Jika muncul ketidakkonsistenan ketika melaksanakan rencana, proses harus ditelaah ulang untuk mencari sumber kesulitan masalah.
• Melihat (mengecek) Kembali Selama langkah ini berlangsung, solusi masalah harus dipertimbangkan. Perhitungan harus dicek kembali. Melakukan pengecekan dapat melibatkan pemecahan yang menentukan akurasi dari komputasi dengan menghitung ulang. Jika membuat estimasi, maka bandingkan dengan solusi. Solusi harus tetap cocok terhadap akar masalah meskipun kelihatan tidak beralasan. Bagian penting dari langkah ini adalah ekstensi. Ini melibatkan pencarian alternatif pemecahan masalah.
• Handayani, Kartika. (2017). Analisis faktor-faktor yang mempengaruhi kemampuan pemecahan masalah soal cerita matematika. SEMNASTIKA 2017, 06 May 2017, Medan.
• Khoiriyah, A. J., & Husamah, H. (2018). Problem-based learning: creative thinking skills, problem-solving skills, and learning outcome of seventh grade students. Jurnal Pendidikan Biologi Indonesia, 4(2), 151–160. https://doi.org/10.22219/jpbi.v4i2.5804
• Made, W. (2016). Strategi Pembelajaran Inovatif Kontemporer. PT Bumi Aksara.
• Mawaddah, Siti. (2015). Kemampuan pemecahan masalah matematika siswa pada pembelajaran matematika dengan menggunakan pembelajaran genaratif (generative learning ) di smp. Jurnal Pendidikan Matematika, 3 (2)
• Tawil, M. & Liliasari. (2013). Berpikir Kompleks. Makassar: Badan Penerbit Universitas Makassar.
• Uno, Hamzah. 2014. Model pembelajaran menciptakan proses belajar mengajar yang kreatif dan efektif. cetakan ke-10. Jakarta: Bumi Aksara.

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Sat / act prep online guides and tips, the 6 scientific method steps and how to use them.

General Education

When you’re faced with a scientific problem, solving it can seem like an impossible prospect. There are so many possible explanations for everything we see and experience—how can you possibly make sense of them all? Science has a simple answer: the scientific method.

The scientific method is a method of asking and answering questions about the world. These guiding principles give scientists a model to work through when trying to understand the world, but where did that model come from, and how does it work?

In this article, we’ll define the scientific method, discuss its long history, and cover each of the scientific method steps in detail.

What Is the Scientific Method?

At its most basic, the scientific method is a procedure for conducting scientific experiments. It’s a set model that scientists in a variety of fields can follow, going from initial observation to conclusion in a loose but concrete format.

The number of steps varies, but the process begins with an observation, progresses through an experiment, and concludes with analysis and sharing data. One of the most important pieces to the scientific method is skepticism —the goal is to find truth, not to confirm a particular thought. That requires reevaluation and repeated experimentation, as well as examining your thinking through rigorous study.

There are in fact multiple scientific methods, as the basic structure can be easily modified.  The one we typically learn about in school is the basic method, based in logic and problem solving, typically used in “hard” science fields like biology, chemistry, and physics. It may vary in other fields, such as psychology, but the basic premise of making observations, testing, and continuing to improve a theory from the results remain the same.

The History of the Scientific Method

The scientific method as we know it today is based on thousands of years of scientific study. Its development goes all the way back to ancient Mesopotamia, Greece, and India.

The Ancient World

In ancient Greece, Aristotle devised an inductive-deductive process , which weighs broad generalizations from data against conclusions reached by narrowing down possibilities from a general statement. However, he favored deductive reasoning, as it identifies causes, which he saw as more important.

Aristotle wrote a great deal about logic and many of his ideas about reasoning echo those found in the modern scientific method, such as ignoring circular evidence and limiting the number of middle terms between the beginning of an experiment and the end. Though his model isn’t the one that we use today, the reliance on logic and thorough testing are still key parts of science today.

The Middle Ages

The next big step toward the development of the modern scientific method came in the Middle Ages, particularly in the Islamic world. Ibn al-Haytham, a physicist from what we now know as Iraq, developed a method of testing, observing, and deducing for his research on vision. al-Haytham was critical of Aristotle’s lack of inductive reasoning, which played an important role in his own research.

Other scientists, including Abū Rayhān al-Bīrūnī, Ibn Sina, and Robert Grosseteste also developed models of scientific reasoning to test their own theories. Though they frequently disagreed with one another and Aristotle, those disagreements and refinements of their methods led to the scientific method we have today.

Following those major developments, particularly Grosseteste’s work, Roger Bacon developed his own cycle of observation (seeing that something occurs), hypothesis (making a guess about why that thing occurs), experimentation (testing that the thing occurs), and verification (an outside person ensuring that the result of the experiment is consistent).

After joining the Franciscan Order, Bacon was granted a special commission to write about science; typically, Friars were not allowed to write books or pamphlets. With this commission, Bacon outlined important tenets of the scientific method, including causes of error, methods of knowledge, and the differences between speculative and experimental science. He also used his own principles to investigate the causes of a rainbow, demonstrating the method’s effectiveness.

Scientific Revolution

Throughout the Renaissance, more great thinkers became involved in devising a thorough, rigorous method of scientific study. Francis Bacon brought inductive reasoning further into the method, whereas Descartes argued that the laws of the universe meant that deductive reasoning was sufficient. Galileo’s research was also inductive reasoning-heavy, as he believed that researchers could not account for every possible variable; therefore, repetition was necessary to eliminate faulty hypotheses and experiments.

All of this led to the birth of the Scientific Revolution , which took place during the sixteenth and seventeenth centuries. In 1660, a group of philosophers and physicians joined together to work on scientific advancement. After approval from England’s crown , the group became known as the Royal Society, which helped create a thriving scientific community and an early academic journal to help introduce rigorous study and peer review.

Previous generations of scientists had touched on the importance of induction and deduction, but Sir Isaac Newton proposed that both were equally important. This contribution helped establish the importance of multiple kinds of reasoning, leading to more rigorous study.

As science began to splinter into separate areas of study, it became necessary to define different methods for different fields. Karl Popper was a leader in this area—he established that science could be subject to error, sometimes intentionally. This was particularly tricky for “soft” sciences like psychology and social sciences, which require different methods. Popper’s theories furthered the divide between sciences like psychology and “hard” sciences like chemistry or physics.

Paul Feyerabend argued that Popper’s methods were too restrictive for certain fields, and followed a less restrictive method hinged on “anything goes,” as great scientists had made discoveries without the Scientific Method. Feyerabend suggested that throughout history scientists had adapted their methods as necessary, and that sometimes it would be necessary to break the rules. This approach suited social and behavioral scientists particularly well, leading to a more diverse range of models for scientists in multiple fields to use.

The Scientific Method Steps

Though different fields may have variations on the model, the basic scientific method is as follows:

#1: Make Observations 

Notice something, such as the air temperature during the winter, what happens when ice cream melts, or how your plants behave when you forget to water them.

#2: Ask a Question

Turn your observation into a question. Why is the temperature lower during the winter? Why does my ice cream melt? Why does my toast always fall butter-side down?

This step can also include doing some research. You may be able to find answers to these questions already, but you can still test them!

#3: Make a Hypothesis

A hypothesis is an educated guess of the answer to your question. Why does your toast always fall butter-side down? Maybe it’s because the butter makes that side of the bread heavier.

A good hypothesis leads to a prediction that you can test, phrased as an if/then statement. In this case, we can pick something like, “If toast is buttered, then it will hit the ground butter-first.”

#4: Experiment

Your experiment is designed to test whether your predication about what will happen is true. A good experiment will test one variable at a time —for example, we’re trying to test whether butter weighs down one side of toast, making it more likely to hit the ground first.

The unbuttered toast is our control variable. If we determine the chance that a slice of unbuttered toast, marked with a dot, will hit the ground on a particular side, we can compare those results to our buttered toast to see if there’s a correlation between the presence of butter and which way the toast falls.

If we decided not to toast the bread, that would be introducing a new question—whether or not toasting the bread has any impact on how it falls. Since that’s not part of our test, we’ll stick with determining whether the presence of butter has any impact on which side hits the ground first.

#5: Analyze Data

After our experiment, we discover that both buttered toast and unbuttered toast have a 50/50 chance of hitting the ground on the buttered or marked side when dropped from a consistent height, straight down. It looks like our hypothesis was incorrect—it’s not the butter that makes the toast hit the ground in a particular way, so it must be something else.

Since we didn’t get the desired result, it’s back to the drawing board. Our hypothesis wasn’t correct, so we’ll need to start fresh. Now that you think about it, your toast seems to hit the ground butter-first when it slides off your plate, not when you drop it from a consistent height. That can be the basis for your new experiment.

#6: Communicate Your Results

Good science needs verification. Your experiment should be replicable by other people, so you can put together a report about how you ran your experiment to see if other peoples’ findings are consistent with yours.

This may be useful for class or a science fair. Professional scientists may publish their findings in scientific journals, where other scientists can read and attempt their own versions of the same experiments. Being part of a scientific community helps your experiments be stronger because other people can see if there are flaws in your approach—such as if you tested with different kinds of bread, or sometimes used peanut butter instead of butter—that can lead you closer to a good answer.

A Scientific Method Example: Falling Toast

We’ve run through a quick recap of the scientific method steps, but let’s look a little deeper by trying again to figure out why toast so often falls butter side down.

#1: Make Observations

At the end of our last experiment, where we learned that butter doesn’t actually make toast more likely to hit the ground on that side, we remembered that the times when our toast hits the ground butter side first are usually when it’s falling off a plate.

The easiest question we can ask is, “Why is that?”

We can actually search this online and find a pretty detailed answer as to why this is true. But we’re budding scientists—we want to see it in action and verify it for ourselves! After all, good science should be replicable, and we have all the tools we need to test out what’s really going on.

Why do we think that buttered toast hits the ground butter-first? We know it’s not because it’s heavier, so we can strike that out. Maybe it’s because of the shape of our plate?

That’s something we can test. We’ll phrase our hypothesis as, “If my toast slides off my plate, then it will fall butter-side down.”

Just seeing that toast falls off a plate butter-side down isn’t enough for us. We want to know why, so we’re going to take things a step further—we’ll set up a slow-motion camera to capture what happens as the toast slides off the plate.

We’ll run the test ten times, each time tilting the same plate until the toast slides off. We’ll make note of each time the butter side lands first and see what’s happening on the video so we can see what’s going on.

When we review the footage, we’ll likely notice that the bread starts to flip when it slides off the edge, changing how it falls in a way that didn’t happen when we dropped it ourselves.

That answers our question, but it’s not the complete picture —how do other plates affect how often toast hits the ground butter-first? What if the toast is already butter-side down when it falls? These are things we can test in further experiments with new hypotheses!

Now that we have results, we can share them with others who can verify our results. As mentioned above, being part of the scientific community can lead to better results. If your results were wildly different from the established thinking about buttered toast, that might be cause for reevaluation. If they’re the same, they might lead others to make new discoveries about buttered toast. At the very least, you have a cool experiment you can share with your friends!

Key Scientific Method Tips

Though science can be complex, the benefit of the scientific method is that it gives you an easy-to-follow means of thinking about why and how things happen. To use it effectively, keep these things in mind!

Don’t Worry About Proving Your Hypothesis

One of the important things to remember about the scientific method is that it’s not necessarily meant to prove your hypothesis right. It’s great if you do manage to guess the reason for something right the first time, but the ultimate goal of an experiment is to find the true reason for your observation to occur, not to prove your hypothesis right.

Good science sometimes means that you’re wrong. That’s not a bad thing—a well-designed experiment with an unanticipated result can be just as revealing, if not more, than an experiment that confirms your hypothesis.

Be Prepared to Try Again

If the data from your experiment doesn’t match your hypothesis, that’s not a bad thing. You’ve eliminated one possible explanation, which brings you one step closer to discovering the truth.

The scientific method isn’t something you’re meant to do exactly once to prove a point. It’s meant to be repeated and adapted to bring you closer to a solution. Even if you can demonstrate truth in your hypothesis, a good scientist will run an experiment again to be sure that the results are replicable. You can even tweak a successful hypothesis to test another factor, such as if we redid our buttered toast experiment to find out whether different kinds of plates affect whether or not the toast falls butter-first. The more we test our hypothesis, the stronger it becomes!

What’s Next?

Want to learn more about the scientific method? These important high school science classes will no doubt cover it in a variety of different contexts.

Test your ability to follow the scientific method using these at-home science experiments for kids !

Need some proof that science is fun? Try making slime

Melissa Brinks graduated from the University of Washington in 2014 with a Bachelor's in English with a creative writing emphasis. She has spent several years tutoring K-12 students in many subjects, including in SAT prep, to help them prepare for their college education.

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Mengenal Apa itu Problem Solving, Manfaat dan Contohnya

Dalam kehidupan sehari-hari, kita sering dihadapkan pada berbagai masalah yang perlu diselesaikan. Dalam hal ini, kemampuan untuk memecahkan masalah atau problem solving adalah suatu keterampilan yang sangat penting. 

Saat ini masih banyak orang yang meremehkan tentang bagaimana cara problem solving yang baik dan benar. Padahal faktanya, problem solving yang buruk bisa berdampak buruk pula. Seperti salah dalam mengambil keputusan besar, hingga perkelahian karena perbedaan pendapat.

Nah, karenanya penting untuk memahami tentang apa itu problem solving, manfaat, hingga cara menerapkannya. 

• 1 Apa Itu Problem Solving?
• 2.1 1. Identifikasi Masalah
• 2.2 2. Pengumpulan Informasi
• 2.3 3. Analisis
• 2.4 4. Pengembangan Solusi
• 2.5 5. Pemilihan Solusi
• 2.6 6. Implementasi
• 2.7 7. Evaluasi
• 3.1 1. Pengembangan Kemampuan Berpikir Kritis
• 3.2 2. Peningkatan Kreativitas
• 3.3 3. Meningkatkan Efisiensi
• 3.4 4. Peningkatan Keterampilan Komunikasi
• 3.5 5. Kepercayaan Diri
• 3.6 6. Pengambilan Keputusan yang Lebih Baik
• 4.1 1. Saat Menumpahkan Air
• 4.2 2. Perencanaan Perjalanan
• 4.3 3. Konflik dengan Rekan Kerja
• 4.4 4. Memecahkan Masalah Matematika
• 5 Mau Mengasah Kemampuan Problem Solving?

Apa Itu Problem Solving?

Problem solving adalah proses kognitif yang melibatkan pemecahan masalah atau menemukan solusi untuk situasi atau permasalahan tertentu. Dalam bahasa Indonesia, kita dapat menyebutnya sebagai “pemecahan masalah.” 

Ini melibatkan pemikiran kreatif, analitis, dan kemampuan untuk mengatasi hambatan. Proses ini umumnya dilakukan untuk mengatasi situasi yang memerlukan solusi, baik dalam kehidupan sehari-hari maupun dalam berbagai konteks, seperti pekerjaan atau pendidikan.

Tahapan Problem Solving

Proses problem solving terdiri dari beberapa tahapan, yaitu:

1. Identifikasi Masalah

Tahap pertama adalah mengidentifikasi masalah dengan jelas. Ini melibatkan pemahaman yang mendalam tentang sifat masalah, penyebabnya, dan dampaknya. Identifikasi masalah yang tepat adalah kunci untuk memulai proses pemecahan masalah.

2. Pengumpulan Informasi

Setelah masalah diidentifikasi, langkah selanjutnya adalah mengumpulkan informasi yang relevan. Informasi ini bisa berasal dari berbagai sumber, termasuk observasi, penelitian, atau wawancara. Pengumpulan informasi membantu dalam memahami akar masalah dan faktor-faktor yang berkontribusi.

3. Analisis

Tahap analisis melibatkan pemikiran kritis dan kemampuan untuk menghubungkan fakta-fakta yang ada. Pada tahap ini, informasi yang telah dikumpulkan dievaluasi dengan cermat untuk memahami sifat masalah secara lebih mendalam.

4. Pengembangan Solusi

Setelah analisis, langkah berikutnya adalah mengembangkan berbagai solusi yang mungkin. Pada tahap ini, kreativitas sangat diperlukan. Solusi yang dihasilkan mungkin bersifat konvensional atau inovatif.

5. Pemilihan Solusi

Dari berbagai solusi yang ada, tahap ini melibatkan pemilihan solusi terbaik yang paling memungkinkan untuk menyelesaikan masalah. Keputusan ini harus didasarkan pada analisis yang baik.

6. Implementasi

Solusi yang dipilih kemudian diimplementasikan. Ini melibatkan tindakan nyata untuk memecahkan masalah. Pada tahap ini, perencanaan yang matang dan pelaksanaan yang efektif penting.

7. Evaluasi

Setelah implementasi, hasilnya dievaluasi. Dalam tahap ini, perlu diperiksa apakah masalah telah terselesaikan atau perlu perubahan lebih lanjut. Evaluasi juga membantu dalam menilai keberhasilan proses pemecahan masalah.

Baca Juga: 5 Metode Problem Solving dan Tips Menghadapi Tantangannya!

Manfaat Problem Solving

Pemecahan masalah memiliki banyak manfaat, terutama dalam konteks kehidupan sehari-hari dan berbagai bidang lainnya. Berikut adalah beberapa manfaat utama dari kemampuan pemecahan masalah:

1. Pengembangan Kemampuan Berpikir Kritis

Pemecahan masalah melibatkan analisis mendalam, evaluasi, dan pemikiran kritis. Ini membantu seseorang untuk menjadi pemikir yang lebih baik dan mampu mengambil keputusan yang lebih baik.

2. Peningkatan Kreativitas

Dalam upaya mencari solusi, pemecahan masalah mendorong seseorang untuk berpikir secara kreatif. Ini dapat menghasilkan solusi yang inovatif dan tidak konvensional.

3. Meningkatkan Efisiensi

Dengan kemampuan pemecahan masalah yang baik, tugas-tugas sehari-hari dapat diselesaikan dengan lebih efisien. Ini menghemat waktu dan sumber daya.

4. Peningkatan Keterampilan Komunikasi

Proses problem solving sering melibatkan berdiskusi dan kolaborasi dengan orang lain, yang dapat meningkatkan keterampilan komunikasi. Ini dapat meningkatkan keterampilan komunikasi interpersonal.

5. Kepercayaan Diri

Menyelesaikan masalah dengan sukses dapat meningkatkan kepercayaan diri seseorang. Mampu mengatasi masalah memberikan rasa pencapaian dan kepuasan pribadi.

6. Pengambilan Keputusan yang Lebih Baik

Kemampuan pemecahan masalah membantu seseorang dalam membuat keputusan yang lebih baik. Dengan analisis yang baik, keputusan yang diambil lebih mungkin membuahkan hasil yang positif.

Dalam rangkaian kehidupan sehari-hari, manfaat pemecahan masalah ini menjadikan keterampilan ini sangat penting. Mulai dari mengatasi masalah sederhana seperti memperbaiki keran yang bocor, hingga menyelesaikan masalah kompleks dalam dunia bisnis, problem solving adalah keterampilan yang bermanfaat.

Baca Juga: Analytical Thinking: Skill yang Paling Dibutuhkan di Dunia Kerja!

Contoh Problem Solving di Kehidupan Sehari-hari

Untuk memberikan pemahaman yang lebih baik, berikut beberapa contoh problem solving dalam kehidupan sehari-hari:

1. Saat Menumpahkan Air

Saat kamu menghadapi tumpahan air di lantai dapur. Kamu akan mengidentifikasi masalahnya, mengambil kain untuk membersihkannya (solusi), dan masalah terselesaikan.

2. Perencanaan Perjalanan

Saat kamu ingin merencanakan liburan keluarga. Dengan mengumpulkan informasi tentang destinasi, transportasi, dan akomodasi, kamu dapat mengembangkan rencana perjalanan yang optimal.

3. Konflik dengan Rekan Kerja

Saat kamu memiliki konflik dengan rekan kerja. Dengan berbicara dengannya dan mencari solusi bersama, kamu dapat mengatasi konflik tersebut.

4. Memecahkan Masalah Matematika

Seorang siswa dihadapkan pada soal matematika yang sulit. Dengan menganalisis soal dan mencari rumus yang sesuai, siswa dapat menyelesaikan soal tersebut.

Baca Juga: Mengenal Apa itu Leadership dan Sikap yang Harus Dimilikinya

Mau Mengasah Kemampuan Problem Solving?

Nah, sekarang Arkawan sudah pahan kan, tentang apa itu problem solving? Jika Arkawan masih bingung atau bahkan ingin mendalami keterampilan tentang problem solving ini, mungkin pelatihan problem solving dari Arkademi ini bisa membantumu!

Pada dasarnya, dalam dunia kerja kita tidak hanya perlu mengasah skill teknikal saja. Softskill seperti problem solving yang satu ini juga sangat dibutuhkan dalam pekerjaan bidang apapun.

Dengan mengidentifikasi masalah, mengumpulkan informasi, menganalisis, mengembangkan solusi, dan mengimplementasikannya, kita dapat mengatasi berbagai permasalahan dengan efektif di dunia kerja.

• Simak Perbedaan Finance dan Accounting Staff, Jangan Salah Pilih!
• Staff Accounting: Pengertian, Skill, Tugas dan Tanggung Jawabnya
• Apa itu Accounting? Pengertian, Fungsi dan Jenis-jenisnya

7 Teknik Analisis Data Kuantitatif dan Langkah-Langkahnya

Teknik analisis data: pengertian, jenis, dan tahapannya, ketahui berapa gaji data analyst dan jenjang kariernya.

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Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about the analysis and evaluation of heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical analysis itself. This essay describes the emergence and development of the philosophical problem of scientific discovery and surveys different philosophical approaches to understanding scientific discovery. In doing so, it also illuminates the meta-philosophical problems surrounding the debates, and, incidentally, the changing nature of philosophy of science.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. pragmatic logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 8.3 heuristic strategies, 9.1 kinds and features of creativity, 9.2 analogy, 9.3 mental models, 10. machine discovery, 11. social epistemology and discovery, 12. integrated approaches to knowledge generation, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broad sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very wide sense, almost all discussions of scientific method and practice could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 18 th century, as philosophy of science and science gradually became two distinct endeavors with different audiences, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, during the 19 th century, the generation of new knowledge came to be clearly and explicitly distinguished from its assessment, and thus the conditions for the narrower notion of discovery as the act or process of conceiving new ideas emerged. This distinction was encapsulated in the so-called “context distinction,” between the “context of discovery” and the “context of justification”.

Much of the discussion about scientific discovery in the 20 th century revolved around this distinction It was argued that conceiving a new idea is a non-rational process, a leap of insight that cannot be captured in specific instructions. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given this assumption, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction between empirical science studies and normative philosophy of science that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. Some philosophers insisted, like their predecessors prior to the 1930s, that the philosopher’s tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view in philosophy of science until the last decades of the 20 th century. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries in past and present inform philosophical thought about the structure and cognitive mechanisms of discovery, but works in psychology, cognitive science, artificial intelligence and related fields have become integral parts of philosophical analyses of the processes and conditions of the generation of new knowledge. Social epistemology has opened up another perspective on scientific discovery, reconceptualizing knowledge generation as group process.

Prior to the 19 th century, the term “discovery” was used broadly to refer to a new finding, such as a new cure, an unknown territory, an improvement of an instrument, or a new method of measuring longitude. One strand of the discussion about discovery dating back to ancient times concerns the method of analysis as the method of discovery in mathematics and geometry, and, by extension, in philosophy and scientific inquiry. Following the analytic method, we seek to find or discover something – the “thing sought,” which could be a theorem, a solution to a geometrical problem, or a cause – by analyzing it. In the ancient Greek context, analytic methods in mathematics, geometry, and philosophy were not clearly separated; the notion of finding or discovering things by analysis was relevant in all these fields.

In the ensuing centuries, several natural and experimental philosophers, including Avicenna and Zabarella, Bacon and Boyle, the authors of the Port-Royal Logic and Newton, and many others, expounded rules of reasoning and methods for arriving at new knowledge. The ancient notion of analysis still informed these rules and methods. Newton’s famous thirty-first query in the second edition of the Opticks outlines the role of analysis in discovery as follows: “As in Mathematicks, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions, but such as are taken from Experiments, or other certain Truths … By this way of Analysis we may proceed from Compounds to Ingredients, and from Motions to the Forces producing them; and in general, from Effects to their Causes, and from particular Causes to more general ones, till the Argument end in the most general. This is the Method of Analysis” (Newton 1718, 380, see Koertge 1980, section VI). Early modern accounts of discovery captured knowledge-seeking practices in the study of living and non-living nature, ranging from astronomy and physics to medicine, chemistry, and agriculture. These rich accounts of scientific inquiry were often expounded to bolster particular theories about the nature of matter and natural forces and were not explicitly labeled “methods of discovery ”, yet they are, in fact, accounts of knowledge generation and proper scientific reasoning, covering topics such as the role of the senses in knowledge generation, observation and experimentation, analysis and synthesis, induction and deduction, hypotheses, probability, and certainty.

Bacon’s work is a prominent example. His view of the method of science as it is presented in the Novum Organum showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimentally produced facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further trials. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. Bacon expounded the procedures of constructing and evaluating tables of presences and absences to underpin his matter theory. In addition, in his other writings, such as his natural history Sylva Sylvarum or his comprehensive work on human learning De Augmentis Scientiarium , Bacon exemplified the “art of discovery” with practical examples and discussions of strategies of inquiry.

Like Bacon and Newton, several other early modern authors advanced ideas about how to generate and secure empirical knowledge, what difficulties may arise in scientific inquiry, and how they could be overcome. The close connection between theories about matter and force and scientific methodologies that we find in early modern works was gradually severed. 18 th - and early 19 th -century authors on scientific method and logic cited early modern approaches mostly to model proper scientific practice and reasoning, often creatively modifying them ( section 3 ). Moreover, they developed the earlier methodologies of experimentation, observation, and reasoning into practical guidelines for discovering new phenomena and devising probable hypotheses about cause-effect relations.

It was common in 20 th -century philosophy of science to draw a sharp contrast between those early theories of scientific method and modern approaches. 20 th -century philosophers of science interpreted 17 th - and 18 th -century approaches as generative theories of scientific method. They function simultaneously as guides for acquiring new knowledge and as assessments of the knowledge thus obtained, whereby knowledge that is obtained “in the right way” is considered secure (Laudan 1980; Schaffner 1993: chapter 2). On this view, scientific methods are taken to have probative force (Nickles 1985). According to modern, “consequentialist” theories, propositions must be established by comparing their consequences with observed and experimentally produced phenomena (Laudan 1980; Nickles 1985). It was further argued that, when consequentialist theories were on the rise, the two processes of generation and assessment of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

More recent research in history of philosophy of science has shown, however, that there was no such sharp contrast. Consequentialist ideas were advanced throughout the 18 th century, and the early modern generative theories of scientific method and knowledge were more pragmatic than previously assumed. Early modern scholars did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured.

During the 18 th and 19 th centuries, the different elements of discovery gradually became separated and discussed in more detail. Discussions concerned the nature of observations and experiments, the act of having an insight and the processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes.

Numerous 19 th -century scholars contributed to these discussions, including Claude Bernard, Auguste Comte, George Gore, John Herschel, W. Stanley Jevons, Justus von Liebig, John Stuart Mill, and Charles Sanders Peirce, to name only a few. William Whewell’s work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, later, much discussed contribution to the philosophical debates about scientific discovery because he explicitly distinguished the creative moment or “happy thought” as he called it from other elements of scientific inquiry and because he offered a detailed analysis of the “discoverer’s induction”, i.e., the pursuit and evaluation of the new insight. Whewell’s approach is not unique, but for late 20 th -century philosophers of science, his comprehensive, historically informed philosophy of discovery became a point of orientation in the revival of interest in scientific discovery processes.

For Whewell, discovery comprised three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin; some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. Colligation involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is extended and iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell’s account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell’s theory of discovery clearly separates three elements: the non-analyzable happy thought or eureka moment; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery. In contrast to many 20 th -century approaches, Whewell’s philosophical conception of discovery also comprises the processes by which the happy thoughts are articulated. Similarly, the process of verification is an integral part of discovery. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell’s conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in 20 th -century philosophical debates. Until the late 20 th century, most philosophers operated with a notion of discovery that is narrower than Whewell’s. In more recent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the happy thought and its initial articulation. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell’s terms) is, or is not, a part of discovery proper – and if it is, whether and how this process is guided by rules. Much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules.

In early 20 th -century logical empiricism, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread. Alternative conceptions of discovery especially in the pragmatist tradition emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed.

In the pragmatist tradition, the term “logic” is used in the broad sense to refer to strategies of human reasoning and inquiry. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: syllogistic) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of an “aha moment”.

Early 20 th -century pragmatic logics of discovery can best be described as comprehensive theories of the mental and physical-practical operations involved in knowledge generation, as theories of “how we think” (Dewey 1910). Among the mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning; among the physical operations are observation and (laboratory) experiments. These features of scientific discovery are either not or only insufficiently represented by traditional syllogistic logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934, see also section 9.2 .). However, in academic philosophy of science, endeavors to develop more systematically the heuristics guiding discovery processes were soon eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different elements of scientific inquiry has a long history but in the first half of the 20 th century, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion between “genuine” philosophy and other fields of science studies, which became potent in philosophy of science. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science, whereby philosophy of science is conceived as a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis. Therefore, the study of scientists’ actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach’s work. Reichenbach’s original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists’ thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the assessment of that theory, specifically the assessment of the theory’s epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then assessed. Rather, generation and assessment are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associated with Karl Popper’s Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant’s quid facti ?) , but only with questions of justification or validity (Kant’s quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7–8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological or sociological theory. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994).

The impact of the context distinction on 20 th -century studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century. The last section shows that there were some attempts to develop logics of discovery in the 1920s and 1930s, especially in the pragmatist tradition. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science and that, therefore, discovery was not a legitimate topic for philosophy of science. Until the last decades of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only during the 1970s did the interest in philosophical approaches to discovery begin to increase again. But the context distinction remained a challenge for philosophies of discovery.

There are several lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and assessments of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and assessment of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

One response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that we cannot but admit a general, domain-neutral logic if we do not want to assume that the success of science is a miracle (Jantzen 2016) and that a logic of scientific discovery can be developed ( section 6 ). Another response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge, but that the process of discovery follows an identifiable, analyzable pattern ( section 7 ).

Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). Yet another response assumes that discovery is or at least involves a creative act. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9.1 ).

All these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating, developing, and assessing the creative thought, as well as the scientific community’s adjudication of what does, and does not, count as “discovery” (Arabatzis 1996). These are the processes that can be examined with the tools of philosophical analysis, augmented by input from other fields of science studies such as sociology, history, or cognitive science.

6. Logics of discovery after the context distinction

One way of responding to the demarcation criterion described above is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists’ reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

• Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
• But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H ’s type to obtain. They would follow as a matter of course from something like H and would be explained by it.
• Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler’s discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson’s reconstruction of the episode is not a historically adequate account of Kepler’s discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson’s schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce’s original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard’s approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. Surprise, for instance, could be characterized as resulting from rapid changes in activation of the node in a neural network representing the “surprising” element (Thagard and Stewart 2011). If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler’s third law (BACON) or the Krebs cycle (KEKADA).

Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). Computational approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, the early computational heuristics have some limitations. Compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1). Because a computer program requires the data from actual experiments, the simulations cover only certain aspects of scientific discoveries; in particular, it cannot determine by itself which data is relevant, which data to relate and what form of law it should look for (Gillies 1996). However, as a consequence of the rise of so-called “deep learning” methods in data-intensive science, there is renewed philosophical interest in the question of whether machines can make discoveries ( section 10 ).

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn’s analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele’s work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn’s view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the creative act is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton’s rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists’ actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Philosophers of biology, for instance, have developed a fine-grained framework to account for the generation and preliminary evaluation of biological mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms. The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is weakly normative in the sense that the strategies for the discovery of mechanisms that were successful in the past may prove useful in future biological research (Darden 2002).

As philosophers of science have again become more attuned to actual scientific practices, interest in heuristic strategies has also been revived. Many analysts now agree that discovery processes can be regarded as problem solving activities, whereby a discovery is a solution to a problem. Heuristics-based methodologies of discovery are neither purely subjective and intuitive nor algorithmic or formalizable; the point is that reasons can be given for pursuing one or the other problem-solving strategy. These rules are open and do not guarantee a solution to a problem when applied (Ippoliti 2018). On this view, scientific researchers are no longer seen as Kuhnian “puzzle solvers” but as problem solvers and decision makers in complex, variable, and changing environments (Wimsatt 2007).

Philosophers of discovery working in this tradition draw on a growing body of literature in cognitive psychology, management science, operations research, and economy on human reasoning and decision making in contexts with limited information, under time constraints, and with sub-optimal means (Gigerenzer & Sturm 2012). Heuristic strategies characterized in these studies, such as Gigerenzer’s “tools to theory heuristic” are then applied to understand scientific knowledge generation (Gigerenzer 1992, Nickles 2018). Other analysts specify heuristic strategies in a range of scientific fields, including climate science, neurobiology, and clinical medicine (Gramelsberger 2011, Schaffner 2008, Gillies 2018). Finally, in analytic epistemology, formal methods are developed to identify and assess distinct heuristic strategies currently in use, such as Bayesian reverse engineering in cognitive science (Zednik and Jäkel 2016).

As the literature on heuristics continues to grow, it has become clear that the term “heuristics” is itself used in a variety of different ways. (For a valuable taxonomy of meanings of “heuristic,” see Chow 2015, see also Ippoliti 2018.) Moreover, as in the context of earlier debates about computational heuristics, debates continue about the limitations of heuristics. The use of heuristics may come at a cost if heuristics introduce systematic biases (Wimsatt 2007). Some philosophers thus call for general principles for the evaluation of heuristic strategies (Hey 2016).

9. Cognitive perspectives on discovery

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing, not on how a novel hypothesis or idea is first thought up. For a long time, the predominant view among philosophers of discovery was that the initial step of discovery is a mysterious intuitive leap of the human mind that cannot be analyzed further. More recent accounts of discovery informed by evolutionary biology also do not explicate how new ideas are formed. The generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988), but the key processes by which new ideas are generated are left unanalyzed.

With the recent rapprochement among philosophy of mind, cognitive science and psychology and the increased integration of empirical research into philosophy of science, these processes have been submitted to closer analysis, and philosophical studies of creativity have seen a surge of interest (e.g. Paul & Kaufman 2014a). The distinctive feature of these studies is that they integrate philosophical analyses with empirical work from cognitive science, psychology, evolutionary biology, and computational neuroscience (Thagard 2012). Analysts have distinguished different kinds and different features of creative thinking and have examined certain features in depth, and from new angles. Recent philosophical research on creativity comprises conceptual analyses and integrated approaches based on the assumption that creativity can be analyzed and that empirical research can contribute to the analysis (Paul & Kaufman 2014b). Two key elements of the cognitive processes involved in creative thinking that have been in the focus of philosophical analysis are analogies ( section 9.2 ) and mental models ( section 9.3 ).

General definitions of creativity highlight novelty or originality and significance or value as distinctive features of a creative act or product (Sternberg & Lubart 1999, Kieran 2014, Paul & Kaufman 2014b, although see Hills & Bird 2019). Different kinds of creativity can be distinguished depending on whether the act or product is novel for a particular individual or entirely novel. Psychologist Margaret Boden distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004). Further distinctions have been proposed, such as anthropological creativity (construed as a human condition) and metaphysical creativity, a radically new thought or action in the sense that it is unaccounted for by antecedents and available knowledge, and thus constitutes a radical break with the past (Kronfeldner 2009, drawing on Hausman 1984).

Psychological studies analyze the personality traits and creative individuals’ behavioral dispositions that are conducive to creative thinking. They suggest that creative scientists share certain distinct personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5).

Recent work on creativity in philosophy of mind and cognitive science offers substantive analyses of the cognitive and neural mechanisms involved in creative thinking (Abrams 2018, Minai et al 2022) and critical scrutiny of the romantic idea of genius creativity as something deeply mysterious (Blackburn 2014). Some of this research aims to characterize features that are common to all creative processes, such as Thagard and Stewart’s account according to which creativity results from combinations of representations (Thagard & Stewart 2011, but see Pasquale and Poirier 2016). Other research aims to identify the features that are distinctive of scientific creativity as opposed to other forms of creativity, such as artistic creativity or creative technological invention (Simonton 2014).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse’s conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse’s approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections again show that the study of scientific discovery integrates different approaches, combining conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, as well as brain imaging techniques (Kounios & Beeman 2009, Thagard & Stewart 2011).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). It cannot design new experiments, instruments, or methods. Subsequent computational research on scientific discovery was driven by the motivation to contribute computational tools to aid scientists in their research (Addis et al. 2016). It appears that computational methods can be used to generate new results leading to refereed scientific publications in astrophysics, cancer research, ecology, and other fields (Langley 2000). However, the philosophical discussion has continued about the question of whether these methods really generate new knowledge or whether they merely speed up data processing. It is also still an open question whether data-intensive science is fundamentally different from traditional research, for instance regarding the status of hypothesis or theory in data-intensive research (Pietsch 2015).

In the wake of recent developments in machine learning, some older discussions about automated discovery have been revived. The availability of vastly improved computational tools and software for data analysis has stimulated new discussions about computer-generated discovery (see Leonelli 2020). It is largely uncontroversial that machine learning tools can aid discovery, for instance in research on antibiotics (Stokes et al, 2020). The notion of “robot scientist” is mostly used metaphorically, and the vision that human scientists may one day be replaced by computers – by successors of the laboratory automation systems “Adam” and “Eve”, allegedly the first “robot scientists” – is evoked in writings for broader audiences (see King et al. 2009, Williams et al. 2015, for popularized descriptions of these systems), although some interesting ethical challenges do arise from “superhuman AI” (see Russell 2021). It also appears that, on the notion that products of creative acts are both novel and valuable, AI systems should be called “creative,” an implication which not all analysts will find plausible (Boden 2014)

Philosophical analyses focus on various questions arising from the processes involving human-machine complexes. One issue relevant to the problem of scientific discovery arises from the opacity of machine learning. If machine learning indeed escapes human understanding, how can we be warranted to say that knowledge or understanding is generated by deep learning tools? Might we have reason to say that humans and machines are “co-developers” of knowledge (Tamaddoni-Nezhad et al. 2021)?

New perspectives on scientific discovery have also opened up in the context of social epistemology (see Goldman & O’Connor 2021). Social epistemology investigates knowledge production as a group process, specifically the epistemic effects of group composition in terms of cognitive diversity and unity and social interactions within groups or institutions such as testimony and trust, peer disagreement and critique, and group justification, among others. On this view, discovery is a collective achievement, and the task is to explore how assorted social-epistemic activities or practices have an impact on the knowledge generated by groups in question. There are obvious implications for debates about scientific discovery of recent research in the different branches of social epistemology. Social epistemologists have examined individual cognitive agents in their roles as group members (as providers of information or as critics) and the interactions among these members (Longino 2001), groups as aggregates of diverse agents, or the entire group as epistemic agent (e.g., Koons 2021, Dragos 2019).

Standpoint theory, for instance, explores the role of outsiders in knowledge generation, considering how the sociocultural structures and practices in which individuals are embedded aid or obstruct the generation of creative ideas. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking (Solomon 2007).

Social epistemologists examining groups as aggregates of agents consider to what extent diversity among group members is conducive to knowledge production and whether and to what extent beliefs and attitudes must be shared among group members to make collective knowledge possible (Bird 2014). This is still an open question. Some formal approaches to model the influence of diversity on knowledge generation suggest that cognitive diversity is beneficial to collective knowledge generation (Weisberg and Muldoon 2009), but others have criticized the model (Alexander et al (2015), see also Thoma (2015) and Poyhönen (2017) for further discussion).

This essay has illustrated that philosophy of discovery has come full circle. Philosophy of discovery has once again become a thriving field of philosophical study, now intersecting with, and drawing on philosophical and empirical studies of creative thinking, problem solving under uncertainty, collective knowledge production, and machine learning. Recent approaches to discovery are typically explicitly interdisciplinary and integrative, cutting across previous distinctions among hypothesis generation and theory building, data collection, assessment, and selection; as well as descriptive-analytic, historical, and normative perspectives (Danks & Ippoliti 2018, Michel 2021). The goal no longer is to provide one overarching account of scientific discovery but to produce multifaceted analyses of past and present activities of knowledge generation in all their complexity and heterogeneity that are illuminating to the non-scientist and the scientific researcher alike.

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abduction | analogy and analogical reasoning | cognitive science | epistemology: social | knowledge: analysis of | Kuhn, Thomas | models in science | Newton, Isaac: Philosophiae Naturalis Principia Mathematica | Popper, Karl | rationality: historicist theories of | scientific method | scientific research and big data | Whewell, William

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• Problem Solving and Decision Making

Prinsip Dasar Memecahkan Masalah (Problem Solving)

• Posted by by Arry Rahmawan
• September 13, 2020

Pada kesempatan kali ini izinkan saya untuk menulis tentang prinsip dasar memecahkan masalah atau problem solving . Ketika artikel ini saya tulis, media-media informasi sedang ramai membahas isu diberlakukannya kembali PSBB di Jakarta karena kembali meningkatnya kasus COVID-19. Di satu sisi ada banyak pihak yang mendukung, namun tidak sedikit juga pihak yang menolaknya. Pihak yang mendukung mengatakan PSBB total akan sangat bermanfaat untuk menurunkan kasus COVID-19, di sisi lain pihak yang menolak mengatakan bahwa PSBB total di Jakarta akan mematikan roda perekonomian dan membuat Indonesia semakin terjerumus ke jurang resesi. Semenjak diumumkan kasus perdana sejak Bulan Maret, total pertumbuhan kasus aktif COVID-19 tidak juga kunjung turun – bahkan naik.

Kasus di atas adalah sebuah kasus riil dari perlunya seseorang memiliki kemampuan  complex problem solving,  atau pemecahan masalah yang kompleks di tingkat negara atau kebijakan. Conn dan McLean (2018) mengungkapkan bahwa  complex problem solving, critical thinking,  dan  creativity  adalah 3 keterampilan terpenting untuk dikuasai di tahun 2020 dan sampai beberapa dekade setelahnya. Saat saya mengajar mata kuliah pengantar kewirausahaan teknologi di Departemen Teknik Industri UI , saya selalu menekankan 3 hal ini kepada mahasiswa, dan mereka banyak saya berikan latihan agar terasah dalam memecahkan masalah, berpikir kritis, dan juga menjadi mahasiswa solutif.

Mengenal Apa Itu Masalah

Lalu, apa itu   problem solving?  Pertama mari kita pahami dulu apa itu masalah.

Apakah Anda tahu, apa yang dimaksud dengan masalah?

Saya yakin selama ini Anda memiliki banyak masalah dalam hidup (begitu juga saya). Tentu kita ingin semua masalah yang ada di hidup kita bisa diselesaikan dengan cepat. Namun, bagaimana kita bisa menyelesaikan masalah, jika kita tidak tahu apa itu masalah (ga bingung kan, hehe)?

Collins Dictionary, mengartikan masalah adalah kondisi yang tidak sesuai dengan yang diharapkan, menyebabkan kesulitan dalam menjalani hidup. Berdasarkan definisi ini, kita tahu bahwa masalah itu adalah adanya  gap  antara “Realita” dan hal “Ideal” yang ingin kita capai.

Sebagai mahasiswa, Anda pasti pernah mengalami ada mata kuliah atau mata pelajaran yang Anda susah sekali mengikutinya. Dosen sudah memberikan batas bawah kelas yaitu Anda harus dapat 60 di ujian. Tapi setelah ikut nilai Anda 40, sehingga Anda tidak lulus. Ada ‘jarak’ antara realita (Anda dapat 40) dan nilai ideal untuk Anda lulus (minimal 60), yang kalau jarak ini tidak dipecahkan Anda tidak lulus dan harus mengulang lagi mata kuliah tersebut di tahun berikutnya.

Upaya Anda untuk menaikkan nilai Anda dari 40 menjadi lebih dari 60 (let’s say, 80) adalah bentuk sederhana dari problem solving.

Mengenal prinsip dasar memecahkan masalah ( problem solving )

Lalu, apa saja prinsip – prinsip yang perlu Anda ketahui dalam memecahkan masalah? Watanabe (2009) dalam bukunya 101 Problem Solving, memetakan ada 4 langkah dasar yang merupakan prinsip problem solving. 4 langkah dasar tersebut dijelaskan di gambar berikut ini,

Saya menggunakan model yang diajukan Watanabe (2009) karena simpel dan juga konsisten dengan beragam literatur lain tentang pemecahan masalah. Intinya, ada 4 hal yang wajib kita lakukan jika kita ingin memecahkan masalah:

1. Memahami situasi atau mendefinisikan masalah dengan baik (understand the situation )

Banyak orang yang tidak bisa memecahkan masalah karena tidak bisa mendefinisikan masalah yang dihadapi dengan baik. Misalnya, “Saya tidak bisa mendapatkan nilai 80 di kelas karena saya tidak punya teman diskusi selama PSBB.”

Mengapa definisi masalah tersebut kurang bagus? Ya, karena definisi masalah tersebut sudah mengandung solusi. Jika masalahnya seperti itu, maka kita tinggal langsung saja cari teman diskusi. Nah, tapi apakah dengan punya teman diskusi nilai kita langsung naik jadi 80? Belum tentu.

Lalu, bagaimana mendefinisikan masalah dengan lebih baik?

Contohnya seperti ini, “Saat ini saya mendapat nilai 40 di mata kuliah X dan saya menargetkan untuk mendapatkan nilai 80 di ujian berikutnya. Hal ini harus saya capai, karena jika di bawah 60 saya harus mengulang kelas lagi yang akan menghabiskan uang sebesar Rpxxxxx dan waktu sebanyak xxxxx jam yang saya miliki.”

Dengan menggunakan definisi masalah tersebut, Anda pun jadi sadar bagaimana kondisi Anda saat ini, apa yang ingin Anda raih, dan apa dampak yang muncul jika Anda tidak meraihnya. Sampai sini paham? Jika kurang paham bisa bertanya di kotak komentar :).

Satu contoh lagi: “Saat ini saya punya hutang satu juta ke X, dan harus mengembalikannya di tanggal 25 September 2020. Jika tidak mengembalikannya, saya akan ditagih dan kepercayaan orang kepada saya menjadi hilang.”

Nah, jika masih belum paham boleh ditanyakan di kotak komentar.

2.  Mengidentifikasi akar penyebabnya (identify the root cause of the problem )

Setelah mendefinisikan masalah, baru kita mencari apa akar penyebab dari masalah kita. Teknik paling mudah adalah dengan menggunakan teknik “5 Why”. Teknik ini adalah dengan bertanya kepada diri kita terkait dengan mengapa kita bisa mendapat nilai jelek, misalnya.

Why 1: Mengapa saya mendapat nilai 40 di ujian matematika? Karena saya banyak salah di konsep geometri

Why 2: Kenapa banyak salah konsep di geometri? Karena saya tidak mempelajarinya dengan sungguh – sungguh

Why 3: Kenapa saya tidak belajar geometri sungguh – sungguh? Karena saya tidak menyukai bagian tersebut

Why 4: Kenapa saya tidak suka? Karena saya tidak tahu apa hubungan geometri dengan cita – cita saya

Why 5: Kenapa saya tidak tahu hubungan geometri dengan cita – cita saya? Karena saya tidak mencari tahu informasi terkait hal itu

Ternyata di sini ‘akar’ masalahnya bukan semata – mata kita tidak suka dengan bagian geometri, tetapi juga kita tidak termotivasi untuk mempelajarinya karena tidak tahu apa manfaatnya. Dengan teknik 5 why ini, kita jadi tahu apa akar masalahnya dan bisa merumuskan alternatif solusi dengan baik.

3.  Memilih dan membuat action plan  (Development of an effective action plan)

Jika sudah dari fase 2, maka fase berikutnya adalah berpikir kreatif dan kritis terhadap alternatif solusi yang mungkin dilakukan. Sebagai contoh:

• Mencari tahu apa manfaat ilmu geometri dalam kehidupan sehari – hari (Googling)
• Menonton film atau movie terkait dengan pentingnya ilmu geometri
• Belajar geometri dengan bantuan video dari internet
• Mengajarkan geometri ke orang lain secara online

Silakan tuliskan alternatif solusi sebanyak – banyaknya dalam fase ini. Kemudian pilih mana yang sekiranya paling efektif untuk menyelesaikan masalah tersebut dengan penggunaan sumber daya yang paling sedikit (hemat waktu dan biaya yang dikeluarkan).

4. Eksekusi solusi secara total, perbaiki jika tidak efektif (Execute and modify, until it is solved)

Jika sudah yakin dengan suatu solusi, maka tahap berikutnya adalah eksekusi secara total. Namun perlu diingat bahwa solusi yang kita terapkan perlu dimonitor dan dievaluasi, apakah sudah efektif? Jika belum, maka kita cari alternatif solusi lain yang lebih efektif dan efisien (hal ini dinamakan iterasi).

Bagaimana jika strateginya sudah efektif dan kita dapat nilai sesuai dengan apa yang ditargetkan? Maka kita tingkatkan target yang lebih tinggi, misal mencapai nilai 100. Hal ini dinamakan dengan  improvement,  dan akan terus seperti itu secara kontinu.

Nah, sampai sini Anda sudah belajar tentang prinsip – prinsip dalam pemecahan masalah, dan juga beberapa tekniknya. Sekarang kita akan membahas apakah prinsip ini bisa dipakai oleh pengambil kebijakan di tengah pandemi COVID-19?

Problem-Solving dan COVID-19

Ilmu problem solving sebenarnya sangat simpel. Kenapa pemerintah atau instansi terkait tidak bisa efektif menyelesaikan masalah COVID-19? Apa mereka tidak menggunakan prinsip ini?

Saya yakin banyak pakar yang menjadi tim ahli di pemerintah dan mereka jauh lebih tahu daripada saya terkait bagaimana penanganan COVID-19 ini.

Satu hal yang perlu dipahami masyarakat adalah, problem solving untuk tatanan negara itu memiliki tingkat kerumitan yang sangat tinggi. Tingkat kerumitannya ada di sifat masalahnya itu sendiri yaitu  multiple problems, actors, interests, uncertainties. 

Multiple problems , di mana masalahnya ada banyak dan multi dimensi. COVID-19 tidak hanya tentang kesehatan, tapi juga ekonomi, sosial, transportasi, dan lain sebagainya. Multiple actors , yaitu masalahnya dimiliki oleh pihak yang beragam, mulai dari presiden, menteri, pemprov, tenaga kesehatan, dan lainnya. Multiple interests , yaitu masalahnya aktor tersebut memiliki kepentingan yang berbeda-beda. Ada yang interestnya menyelamatkan rakyat, dengan mengurangi mortality rate, ada yang interestnya mendapatkan keuntungan, dsb Multiple uncertainties ,  yaitu ketidakpastian yang menghadang di masa depan macam – macam, mulai dari kemunculan virus baru, perilaku masyarakat yang tiba – tiba susah diatur, di luar kapasitas dari pemerintah sebagai pengambil kebijakan. Multiple rationalities ,  yaitu setiap aktor yang terlibat memiliki rasionalitas yang berbeda dalam memandang masalah. Ada yang dia berbasis pada data karena suka membaca, ada yang berbasis pada bisikan karena dia minta tolong dibacakan staf ahli, dan ada yang berbasis intuisi karena dia sudah merasa berpengalaman menangani hal – hal tersebut di masa lalu.

Kelima faktor itu masing – masing saling terkoneksi satu sama lain, menyebabkan masalah megakompleks yang sedang dihadapi oleh Indonesia saat ini. Jujur kadang saya seringkali gemas dengan netizen sok tahu yang menggampangkan cara pengambilan keputusan di tingkat wilayah atau nasional yang mega kompleks ini, padahal cara pengambilan kebijakan di negara tidak sesederhana menyelesaikan masalah Anda mau masuk kampus mana dan memilih jurusan apa untuk melanjutkan studi.

Namun, berkaca dari prinsip problem solving yang saya jelaskan tadi, saya jadi kepikiran satu hal. Apakah carut marutnya penanganan COVID-19 di Indonesia karena kita tidak memiliki atau tidak tahu apa masalah yang kita hadapi sebagai suatu bangsa? Apakah belum ada definisi masalah yang jelas (fase 1) yang bisa disepakati oleh satu bangsa untuk kita perjuangkan bersama menyelesaikan masalah tersebut?

Apakah kita bisa memiliki satu atau  single problem statement,  yang mana itu menjadi masalah yang kita harus selesaikan bersama sebagai satu bangsa? Jadi apapun peran kita di negara saat ini, single problem statement  tersebut mewakili semua kepentingan kita, sehingga kita berfokus saja untuk menyelesaikan masalah itu agar Indonesia bisa menyelesaikan penanganan COVID-19 dengan lebih baik.

Jika belum ada dan tidak mencoba ditemukan, maka Indonesia dalam kondisi saat ini belum melewati fase 1 dari tahap penyelesaian masalah dan buat saya itu mengerikan.

Jika ada yang bisa merumuskannya, saya yakin Anda akan sangat berjasa kepada negara karena besar kemungkinan Anda dapat mempersatukan bangsa.

Semoga artikel ini bisa sedikit membuka jalan agar kita bisa menjadi seorang pengambil keputusan yang lebih bijaksana.

Salam, Arry Rahmawan

Arry Rahmawan

Arry Rahmawan adalah seorang pembelajar yang memiliki ketertarikan dalam mempelajari ilmu tentang produktivitas hidup, entrepreneurship, dan pengembangan bisnis. Sejak tahun 2012, Arry rutin menulis dan membuat konten terkait tiga topik tersebut di blog ini. Arry menamatkan pendidikan S1 dan S2 nya di Departemen Teknik Industri, Universitas Indonesia dan menjadi dosen tetap non-PNS di Departemen yang sama sejak tahun 2016. Untuk meningkatkan kapasitas keilmuannya, Arry banyak mengambil sertifikasi, workshop, course, dan mentorship dari berbagai institusi kelas dunia. Selain aktif mengajar di UI dan beberapa kampus di Indonesia, Arry juga berpengalaman menjadi konsultan, trainer, dan coach independen untuk ketiga topik yang diminatinya tersebut. Klien yang sudah ditanganinya sangat beragam, mulai dari instansi pemerintahan, kementerian, BUMN, korporasi/swasta, lembaga pendidikan, serta lembaga non-profit. Saat ini Arry berdomisili di Belanda dalam rangka tugas belajar di Delft University of Technology, Faculty of Technology, Policy, and Management. Untuk menghubunginya, silakan kontak melalui direct message LinkedIn atau Instagram

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3 Langkah Melatih Berpikir Inovatif

• October 19, 2012
• 2 minute read

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'Third Millennium Thinking': How to use scientific tools to solve everyday problems

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Life is full of decisions. “ Third Millennium Thinking: Creating Sense in a World of Knowledge ” outlines methods of making choices rationally using scientific methods.

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Book excerpt: 'Third Millennium Thinking: Creating Sense in a World of Nonsense'

By Saul Perlmutter, John Campbell and Robert MacCoun

INTRODUCTION

In just the past few decades, those of us who live in the internet-​connected world have obtained access to a nearly unfathomable amount of information. We can click a link and instantly gain insight into whatever we’re curious about, whether it’s treatment options for a particular health condition, how to build a solar generator, or the political history of Malta. On the other hand, sometimes there is so much information we don’t know how to sort or evaluate it. The social science database ProQuest, for example, boasts of “a growing content collection that now encompasses . . . 6 billion digital pages and spans six centuries.” And that’s just old-​school, print information! The Internet Archive’s Wayback Machine, an archive of websites and other digital artifacts dating back to 1996, hosts almost a trillion pages of digital content, tens of millions of books and audios, and nearly a million software programs.

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To make a sound decision, take a meaningful action, or solve a problem — whether as individuals, in groups, or as a society — we need first to understand reality. But when reality is not easy to discern, and we’re not sure which experts to trust to clarify the matter, we adopt other strategies for navigating the clutter. We “go with our gut”; decide what we “believe” and look for evidence to reaffirm whatever that is; adopt positions based on our affiliations with people we know; even find reassurance in belittling the people who disagree with us. We choose to consult experts who tell us what we like to hear; or bond in shared mistrust of people providing or communicating the information that confuses us, whether they are scientists, scholars, journalists, community leaders, policymakers, or other experts. These coping strategies may help us get by in our personal or professional lives; they may provide a consoling sense of identity or belonging. But they do not actually help us see clearly or make good decisions. And resorting to them can have dangerous social and political consequences.

How can we navigate better — as individuals, and as a society — in this age of informational overwhelm? How do we ward off confusion, avoid mental traps, and sift sense out of nonsense? How do we make decisions and solve problems collaboratively with people who interpret information differently or have different values than we do?

The three of us — a physicist (Saul), a philosopher ( John), and a psychologist (Rob) — have been working closely together for nearly a decade on a project to help our students learn to think about big problems and make effective decisions in this “too much information” age. We began our collaboration in 2011, in response to what was already a worrying trend toward no‑think, politics-​driven decision-​making. An issue like raising the national debt ceiling, for example, was being debated that summer as if it were a religious schism, rather than a simple, practical, probably even testable question of what economic approach would work best to improve the country’s economic well-being. Most of the arguments both yea and nay betrayed equal disregard for, or ignorance of, the most basic principles of scientific thought. We began to wonder whether it might be possible to first articulate and then teach the principles that would lead to clearer thinking, more rational arguments, and a more fruitful collaborative decision-​making process.

The result was a team-​taught, multidisciplinary Big Ideas course at UC Berkeley, intended to teach students the whole gamut of ideas, tools, and approaches that natural and social scientists use to understand the world. We also designed the course to show how useful these approaches can be for everybody in day‑to‑day life, whether working individually or collaboratively, in making reasoned decisions and solving the full range of problems that face us. To our great satisfaction, the course has been both popular and successful, and has since been replicated and adapted by other teachers at a growing number of other universities.1 Our students appear to rethink their worlds and emerge energized with new ways to approach both personal decision-​making and our society’s problems. They are better able to investigate their questions, evaluate information and expertise, and work together as members of a group or a society. Inspired by their enthusiasm, we began to think about new ways to share these tools — and this new way of thinking and working together — beyond the classroom, with students and citizens of all ages.

We have become ever more concerned that our society is losing its way, causing suffering — and missing great opportunities — simply because we don’t have the tools that could help us make sense of the extraordinary amount of complex, often contradictory information now available to us. Practical problem-​solving can come to a standstill when we cannot ascertain the facts of the problems, or, when those problems require communal or political solutions, even agree with others on what those facts are. We humans, who can figure out rocket science and fly to the moon, can’t always figure out how to navigate uncertainty and conflicting points of view to make a simple reasonable decision when we need to.

Part of the problem is that science itself is often a major source of the highly technical, opaque, inconsistent, and contradictory information that has overwhelmed, perplexed, and even angered people. Trust in science has eroded in the recent past.2 The achievements of science cannot live up to all the utopian expectations those successes have generated. Some scientific achievements have also come with negative social, political, or environmental side effects. For these and other reasons, science has become one of the totems of polarization in political discussions. In short, as science became harder to understand, was connected to undesirable side effects, and subjected to politically partisan critiques, many people lost their trust in scientists and in “science” itself.3

But science also has a phenomenal record of providing insight into — if not answers to — the most confounding questions humans have thought to ask. It has helped us to solve puzzles, address problems, and make better lives over millennia. It is a culture of inquiry rooted in the dawn of humankind, with centuries of practice in evaluating conflicting information in a baffling world, and in distinguishing what we know from what we don’t. Along the way, scientists have learned from both successes and mistakes, breakthroughs and blunders, to refine the tools with which to address new questions and solve new problems.

Over the past few years, we have all become aware of the shocking degree of polarization in our society, and the surprising interaction between this polarization and our society’s often-​problematic relationship to science and scientific expertise. If we are to have any hope of finding the practical common plans and common understandings that can move our society ahead together, we need to learn to accept the possibility of errors in our own thinking, and our need for opposing views that help us see where we are going wrong. And we need to understand the source of the disenchantment with and backlash against scientific progress that arose during the end of the Second Millennium and seek to repair it.

No one book and no single approach can heal the rifts. Not all of our polarized disagreements will vanish. But we have to start somewhere. And we believe that one of our more promising starting points is with the culture of science — if we begin to borrow its tools, ideas, and processes, and make a Third Millennium shift in our own thinking.

Adapted from "Third Millenium Thinking" by Saul Perlmutter, John Campbell, and Robert MacCoun. Copyright © 2024 by Saul Perlmutter, John Campbell, and Robert MacCoun. Used with permission of Little, Brown Spark, an imprint of Little, Brown and Company. New York, NY. All rights reserved.

Emiko Tamagawa  produced and edited this interview for broadcast with  Todd Mundt .  Grace Griffin  adapted it for the web.

This article was originally published on March 26, 2024.

This segment aired on March 26, 2024.

Scott Tong Co-Host, Here & Now Scott Tong joined Here & Now as a co-host in July 2021 after spending 16 years at Marketplace as Shanghai bureau chief and senior correspondent.

Emiko Tamagawa Senior Producer, Here & Now Emiko Tamagawa produces arts and culture segments for Here & Now.

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