problem solving skills french

World Languages and Cultures

Why french.

To be understood in 55 countries across five continents and by over 200 millions.

French is the third most common language on the internet. French is one of the largest language spoken on the African continent by sheer number of French speakers; and six of 10 fastest growing economy in the world are located in Africa. By speaking French you expand to opportunities from a business perspective.

To get a head and start on learning other romance languages like Spanish, Italian, Portuguese and Romanian.

To develop critical, creative thinking and problem solving skills. French also provides the base for more than 50% of the modern English vocabulary, which improves performance on standardized tests.

To be more competitive in the National and international job market in disciplines like business, medicine, aviation, law, transportation technologies, global/international distribution, luxury goods, tourism and restaurant/food business.

French is the official working language of the UN, NATO, UNESCO, the Olympic Committee, the European Union, the International Red Cross etc...

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Dr. Karen Wallace

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Locke Hall 314

BA and Minor in French

Students interested in pursuing a more in depth study of the French language and Francophone cultures can choose to major or minor in French.  Click on the link below for more information:

Major or Minor in French

Foreign Language Requirements

Most students at Howard University have a foreign language requirement to graduate.  Depending on their major, students typically take between two to four semesters of a foreign language.  Enrolling in French language courses at Howard will count towards the foreign language requirement.  The course sequence for the basic French language courses is as follows:

French I - (FREN 001) 

French II - (FREN 002)

French III - (FREN 003)

French IV - (FREN 004)

French translation of 'problem-solving'

• problem-solving

Examples of 'problem-solving' in a sentence problem-solving

Trends of problem-solving.

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• problem child
• problem page
• problem-free
• problematic
• problematic situation
• problematical
• All ENGLISH words that begin with 'P'

Related terms of problem-solving

• an approach to problem-solving

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What is the translation of "problem-solving skills" in French?

"problem-solving skills" in french.

• volume_up compétences en résolution de problèmes

Context sentences

Similar translations, english contextual examples of "problem-solving skills" in english.

These sentences come from external sources and may not be accurate. bab.la is not responsible for their content.

• open_in_new Link to source
• warning Request revision

English French Contextual examples of "problem-solving skills" in French

Similar translations for "problem-solving skills" in french.

• compétences
• compétences en communication
• habiletés en communication
• capacités de communication
• jeu d'acteur
• compétences analytiques
• compétences en informatique
• une bonne pratique de la dactylo
• connaissances en traitement de texte
• compétences linguistiques
• compétences en matière de gestion
• techniques d'expression orale
• pratique de la dactylo
• recyclage professionnel
• problem stem
• problem worsen
• problem-based learning
• problem-solving
• problem-solving approach
• problem-solving capabilities
• problem-solving exercise
• problem-solving methods
• problem-solving process
• problem-solving session
• problem-solving skills
• problem-solving strategies
• problem-solving tasks
• problem-solving techniques
• problem-solving test
• problem-solving tool
• problematic
• problematic aspect
• problematic assumption
• problematic behaviour
• problematic elements

Do you want to translate into other languages? Have a look at our English-Swedish dictionary .

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Universities Have a Computer-Science Problem

The case for teaching coders to speak French

Listen to this article

Produced by ElevenLabs and News Over Audio (NOA) using AI narration.

Updated at 5:37 p.m. ET on March 22, 2024

Last year, 18 percent of Stanford University seniors graduated with a degree in computer science, more than double the proportion of just a decade earlier. Over the same period at MIT, that rate went up from 23 percent to 42 percent . These increases are common everywhere: The average number of undergraduate CS majors at universities in the U.S. and Canada tripled in the decade after 2005, and it keeps growing . Students’ interest in CS is intellectual—culture moves through computation these days—but it is also professional. Young people hope to access the wealth, power, and influence of the technology sector.

That ambition has created both enormous administrative strain and a competition for prestige. At Washington University in St. Louis, where I serve on the faculty of the Computer Science & Engineering department, each semester brings another set of waitlists for enrollment in CS classes. On many campuses, students may choose to study computer science at any of several different academic outposts, strewn throughout various departments. At MIT, for example, they might get a degree in “Urban Studies and Planning With Computer Science” from the School of Architecture, or one in “Mathematics With Computer Science” from the School of Science, or they might choose from among four CS-related fields within the School of Engineering. This seepage of computing throughout the university has helped address students’ booming interest, but it also serves to bolster their demand.

Another approach has gained in popularity. Universities are consolidating the formal study of CS into a new administrative structure: the college of computing. MIT opened one in 2019. Cornell set one up in 2020. And just last year, UC Berkeley announced that its own would be that university’s first new college in more than half a century. The importance of this trend—its significance for the practice of education, and also of technology—must not be overlooked. Universities are conservative institutions, steeped in tradition. When they elevate computing to the status of a college, with departments and a budget, they are declaring it a higher-order domain of knowledge and practice, akin to law or engineering. That decision will inform a fundamental question: whether computing ought to be seen as a superfield that lords over all others, or just a servant of other domains, subordinated to their interests and control. This is, by no happenstance, also the basic question about computing in our society writ large.

When I was an undergraduate at the University of Southern California in the 1990s, students interested in computer science could choose between two different majors: one offered by the College of Letters, Arts and Sciences, and one from the School of Engineering. The two degrees were similar, but many students picked the latter because it didn’t require three semesters’ worth of study of a (human) language, such as French. I chose the former, because I like French.

An American university is organized like this, into divisions that are sometimes called colleges , and sometimes schools . These typically enjoy a good deal of independence to define their courses of study and requirements as well as research practices for their constituent disciplines. Included in this purview: whether a CS student really needs to learn French.

The positioning of computer science at USC was not uncommon at the time. The first academic departments of CS had arisen in the early 1960s, and they typically evolved in one of two ways: as an offshoot of electrical engineering (where transistors got their start), housed in a college of engineering; or as an offshoot of mathematics (where formal logic lived), housed in a college of the arts and sciences. At some universities, including USC, CS found its way into both places at once.

The contexts in which CS matured had an impact on its nature, values, and aspirations. Engineering schools are traditionally the venue for a family of professional disciplines, regulated with licensure requirements for practice. Civil engineers, mechanical engineers, nuclear engineers, and others are tasked to build infrastructure that humankind relies on, and they are expected to solve problems. The liberal-arts field of mathematics, by contrast, is concerned with theory and abstraction. The relationship between the theoretical computer scientists in mathematics and the applied ones in engineers is a little like the relationship between biologists and doctors, or physicists and bridge builders. Keeping applied and pure versions of a discipline separate allows each to focus on its expertise, but limits the degree to which one can learn from the other.

Read: Programmers, stop calling yourself engineers

By the time I arrived at USC, some universities had already started down a different path. In 1988, Carnegie Mellon University created what it says was one of the first dedicated schools of computer science. Georgia Institute of Technology followed two years later. “Computing was going to be a big deal,” says Charles Isbell, a former dean of Georgia Tech’s college of computing and now the provost at the University of Wisconsin-Madison. Emancipating the field from its prior home within the college of engineering gave it room to grow, he told me. Within a decade, Georgia Tech had used this structure to establish new research and teaching efforts in computer graphics, human-computer interaction, and robotics. (I spent 17 years on the faculty there, working for Isbell and his predecessors, and teaching computational media.)

Kavita Bala, Cornell University’s dean of computing, told me that the autonomy and scale of a college allows her to avoid jockeying for influence and resources. MIT’s computing dean, Daniel Huttenlocher, says that the speed at which computing evolves justifies the new structure.

But the computing industry isn’t just fast-moving. It’s also reckless. Technology tycoons say they need space for growth, and warn that too much oversight will stifle innovation. Yet we might all be better off, in certain ways, if their ambitions were held back even just a little. Instead of operating with a deep understanding or respect for law, policy, justice, health, or cohesion, tech firms tend to do whatever they want . Facebook sought growth at all costs, even if its take on connecting people tore society apart . If colleges of computing serve to isolate young, future tech professionals from any classrooms where they might imbibe another school’s culture and values—engineering’s studied prudence, for example, or the humanities’ focus on deliberation—this tendency might only worsen.

Read: The moral failure of computer scientists

When I raised this concern with Isbell, he said that the same reasoning could apply to any influential discipline, including medicine and business. He’s probably right, but that’s cold comfort. The mere fact that universities allow some other powerful fiefdoms to exist doesn’t make computing’s centralization less concerning. Isbell admitted that setting up colleges of computing “absolutely runs the risk” of empowering a generation of professionals who may already be disengaged from consequences to train the next one in their image. Inside a computing college, there may be fewer critics around who can slow down bad ideas. Disengagement might redouble. But he said that dedicated colleges could also have the opposite effect. A traditional CS department in a school of engineering would be populated entirely by computer scientists, while the faculty for a college of computing like the one he led at Georgia Tech might also house lawyers, ethnographers, psychologists, and even philosophers like me. Huttenlocher repeatedly emphasized that the role of the computing college is to foster collaboration between CS and other disciplines across the university. Bala told me that her college was established not to teach CS on its own but to incorporate policy, law, sociology, and other fields into its practice. “I think there are no downsides,” she said.

Mark Guzdial is a former faculty member in Georgia Tech’s computing college, and he now teaches computer science in the University of Michigan’s College of Engineering. At Michigan, CS wasn’t always housed in engineering—Guzdial says it started out inside the philosophy department, as part of the College of Literature, Science and the Arts. Now that college “wants it back,” as one administrator told Guzdial. Having been asked to start a program that teaches computing to liberal-arts students, Guzdial has a new perspective on these administrative structures. He learned that Michigan’s Computer Science and Engineering program and its faculty are “despised” by their counterparts in the humanities and social sciences. “They’re seen as arrogant, narrowly focused on machines rather than people, and unwilling to meet other programs’ needs,” he told me. “I had faculty refuse to talk to me because I was from CSE.”

In other words, there may be downsides just to placing CS within an engineering school, let alone making it an independent college. Left entirely to themselves, computer scientists can forget that computers are supposed to be tools that help people. Georgia Tech’s College of Computing worked “because the culture was always outward-looking. We sought to use computing to solve others’ problems,” Guzdial said. But that may have been a momentary success. Now, at Michigan, he is trying to rebuild computing education from scratch, for students in fields such as French and sociology. He wants them to understand it as a means of self-expression or achieving justice—and not just a way of making software, or money.

Early in my undergraduate career, I decided to abandon CS as a major. Even as an undergraduate, I already had a side job in what would become the internet industry, and computer science, as an academic field, felt theoretical and unnecessary. Reasoning that I could easily get a job as a computer professional no matter what it said on my degree, I decided to study other things while I had the chance.

I have a strong memory of processing the paperwork to drop my computer-science major in college, in favor of philosophy. I walked down a quiet, blue-tiled hallway of the engineering building. All the faculty doors were closed, although the click-click of mechanical keyboards could be heard behind many of them. I knocked on my adviser’s door; she opened it, silently signed my paperwork without inviting me in, and closed the door again. The keyboard tapping resumed.

The whole experience was a product of its time, when computer science was a field composed of oddball characters, working by themselves, and largely disconnected from what was happening in the world at large. Almost 30 years later, their projects have turned into the infrastructure of our daily lives. Want to find a job? That’s LinkedIn. Keep in touch? Gmail, or Instagram. Get news? A website like this one, we hope, but perhaps TikTok. My university uses a software service sold by a tech company to run its courses. Some things have been made easier with computing. Others have been changed to serve another end, like scaling up an online business.

Read: So much for ‘learn to code’

The struggle to figure out the best organizational structure for computing education is, in a way, a microcosm of the struggle under way in the computing sector at large. For decades, computers were tools used to accomplish tasks better and more efficiently. Then computing became the way we work and live. It became our culture, and we began doing what computers made possible, rather than using computers to solve problems defined outside their purview. Tech moguls became famous, wealthy, and powerful. So did CS academics (relatively speaking). The success of the latter—in terms of rising student enrollments, research output, and fundraising dollars—both sustains and justifies their growing influence on campus.

If computing colleges have erred, it may be in failing to exert their power with even greater zeal. For all their talk of growth and expansion within academia, the computing deans’ ambitions seem remarkably modest. Martial Hebert, the dean of Carnegie Mellon’s computing school, almost sounded like he was talking about the liberal arts when he told me that CS is “a rich tapestry of disciplines” that “goes far beyond computers and coding.” But the seven departments in his school correspond to the traditional, core aspects of computing plus computational biology. They do not include history, for example, or finance. Bala and Isbell talked about incorporating law, policy, and psychology into their programs of study, but only in the form of hiring individual professors into more traditional CS divisions. None of the deans I spoke with aspires to launch, say, a department of art within their college of computing, or one of politics, sociology, or film. Their vision does not reflect the idea that computing can or should be a superordinate realm of scholarship, on the order of the arts or engineering. Rather, they are proceeding as though it were a technical school for producing a certain variety of very well-paid professionals. A computing college deserving of the name wouldn’t just provide deeper coursework in CS and its closely adjacent fields; it would expand and reinvent other, seemingly remote disciplines for the age of computation.

Near the end of our conversation, Isbell mentioned the engineering fallacy, which he summarized like this: Someone asks you to solve a problem, and you solve it without asking if it’s a problem worth solving. I used to think computing education might be stuck in a nesting-doll version of the engineer’s fallacy, in which CS departments have been asked to train more software engineers without considering whether more software engineers are really what the world needs. Now I worry that they have a bigger problem to address: how to make computer people care about everything else as much as they care about computers.

This article originally mischaracterized the views of MIT’s computing dean, Daniel Huttenlocher. He did not say that computer science would be held back in an arts-and-science or engineering context, or that it needs to be independent.

Morning Carpool

21 Mental Shifts to Boost Problem-Solving Skills and Become More Strategic

Posted: February 10, 2024 | Last updated: February 10, 2024

Discover transformative mental shifts to supercharge your problem-solving skills. From embracing uncertainty to the power of daydreaming, prepare to change the way you tackle challenges forever!

Embrace Uncertainty

Accept that not all answers are immediately clear. Uncertainty can be a powerful motivator rather than a source of stress. By embracing the unknown, we open ourselves up to a broader range of possibilities and solutions.

Seek Diverse Perspectives

Look beyond your own experiences. Different perspectives can provide unique insights and spark innovative solutions. Engaging with people from various backgrounds allows you to see problems through a new lens and discover paths you might not have considered.

Simplify the Complex

Break down big problems into smaller, manageable parts. When faced with a complex issue, deconstruct it to understand its fundamental components. This approach makes the problem less daunting and easier to tackle, leading to clearer, more effective solutions.

Adopt a Growth Mindset

Believe in your ability to learn and grow. A growth mindset encourages resilience and the pursuit of knowledge. Challenges are just undiscovered opportunities with potential for personal and professional development.

Question Assumptions

Challenge the status quo. The barriers to solving a problem are often based on outdated or incorrect assumptions. By questioning the basis of your thinking, you can uncover new paths and innovative solutions.

Think in Reverse

Start with the desired outcome and work backward. This reverse-engineering approach forces you to think differently and can reveal insights you might have missed when approaching the problem linearly.

Embrace Failure as a Teacher

Learn from mistakes and change your perspective. Nobody likes to fail, but each failure provides valuable lessons that can guide future decisions and strategies. Failure isn’t the end but the beginning of understanding.

Harness the Power of Daydreaming

Let your mind wander. Sometimes, the best ideas come when you’re not actively trying to solve a problem. Allowing your mind to drift can lead to creative breakthroughs and unexpected solutions.

Practice Empathy

Understand others’ perspectives and needs. By putting yourself in someone else’s shoes, you can gain insights into the emotional and practical aspects of a problem, leading to more compassionate and effective solutions.

Set Clear Goals

Define what success looks like. Clear goals provide direction and focus, making identifying the steps needed to solve a problem easier. They also help measure progress and keep you motivated.

Stay Curious

Ask questions and seek knowledge. A curious mind is always looking for new information and ideas, which can lead to innovative problem-solving strategies. Curiosity is the engine of achievement.

Use Analogies

Draw parallels from different areas. Analogies can help clarify complex problems by relating them to something more familiar. This can simplify the problem-solving process and spark creative solutions.

Focus on the Process, Not Just the Outcome

Enjoy the journey of problem-solving. Focusing too much on the end result can lead to frustration and missed opportunities. By valuing the process, you can learn and adapt as you go, leading to more sustainable solutions.

Prioritize Effectively

Set deadlines for achieving your goals. Know what matters most. Not all aspects of a problem are equally important. By prioritizing the key factors, you can allocate your time and resources more effectively and achieve better results.

Build Resilience

Give yourself time to recover, then bounce back from setbacks. Resilience is crucial for problem-solving, as it allows you to keep going despite challenges and failures. Resilience turns problems into opportunities.

Cultivate Patience

Give solutions time to unfold. Sometimes, the best solutions emerge over time, and immediate answers aren’t always the best. Patience allows you to thoroughly explore options and make more considered decisions.

Practice Reflection

Don’t overlook the power of self-reflection. Take time to think about what you’ve learned. Reflecting on your experiences and the outcomes of your problem-solving efforts can provide valuable insights and improve future strategies.

Encourage Collaboration

Work with others to find solutions and share goals. Collaborating with a team can bring in a range of skills and perspectives that enhance the problem-solving process and lead to more effective solutions.

Visualize Success

Imagine the desired outcome. Visualization can be a powerful motivator to enhance your performance and guide your actions toward achieving your goals. Focusing on the end result in your mind’s eye can make it a reality.

Adapt and Evolve

Be willing to change your approach. The most effective problem-solvers are flexible and open to new methods and ideas. Adapting your strategy in response to new information or challenges can lead to better solutions.

Maintain a Positive Attitude

Stay optimistic and focused. A positive outlook can keep you motivated and open to new ideas. An optimistic mindset can also make the problem-solving process more enjoyable and less daunting.

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