what is problem solving skill pdf

• Corpus ID: 60967988

The ideal problem solver. A guide for improving thinking, learning, and creativity

• J. Bransford , B. Stein
• Published 1984
• Education, Psychology

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Toward a design theory of problem solving, beliefs , processes and difficulties associated with mathematical problem solving of grade 9 students, essential components for solving various problems in multimedia learning environments., designing collaborative learning sessions that promote creative problem solving using design patterns, integrating metacognitive prompts and critical inquiry process display to support development of problem-solving skills, implementation of problem solving with concept map to improve critical thinking skills and chemistry learning achievement, development and validation of a problem solving skill test in robot programming using scaffolding tools.

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IDEAL Problem Solving dalam Pembelajaran Matematika

Towards a comprehensive account of effective thinking, cognitive variables in science problem solving: a review of research, 13 references, lateral thinking: creativity step by step, the mind's best work.

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The Art of Creative Thinking

The fabric of the heavens: the development of astronomy and dynamics, using idea checklists with college students: overcoming resistance, reasoning in humans. ii. the solution of a problem and its appearance in consciousness., cognitive psychology : thinking and creating, the prince . and, management and machiavelli : an inquiry into the politics of corporate life, related papers.

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What is problem solving?

2002, The connected teacher's companion to problem solving in science and mathematics

Related Papers

Mbuthia Ngunjiri

A widely held goal of mathematics instruction is the improvement of students problem solving performance. These are many variables that may affect the problem solving performance. They include the problem, the problem solver, the problem solving processes, and the problem solving environment. In mathematics instruction, students are largely unaware of the processes involved in problem solving and addressing this issue is important. This paper focuses on mental processes involved in mathematical problem solving.

Janine Remillard

Frank Lester

The Proceedings of the 12th International Congress on Mathematical Education

Zahra Gooya

The Journal of Mathematical Behavior

Joanna Mamona

ABSTRACT Introduces this special issue of the Journal of Mathematical Behavior. This special issue originated from the 10th International Congress of Mathematics Education's Topic Study Group 18: Problem Solving in Mathematics Education. The general aims of the Topic Study Group were to provide a forum for those who are interested in any aspect of problem-solving research at any educational level, to present recent findings, and to exchange ideas. We set up three specific goals for the Problem Solving Topic Study Group: (1) to examine the understanding of the complex cognitive processes involved in problem solving; (2) to explore the actual mechanisms by which students learn and make sense of mathematics through problem solving, and how this can be supported by teachers; and (3) to identify future directions of problem-solving research, including the use of information technology. The Topic Study Group received a good response. Most of the papers in this special issue are from those who presented at the Topic Study Group. In addition, we invited a few other researchers to submit papers in order to cover various aspects of problem-solving research that we wished to be represented in this issue. This special issue includes 12 papers, each addressing at least one of the three goals listed above. The first six papers that appear are empirically based; in these papers, the authors present the results of the fieldwork that they have conducted and also raise research questions for future studies. The remaining six papers are essays discussing issues about problem solving, and how these issues have been, or should be, the subjects of research. In this article, we briefly highlight the contributions of each of the 12 papers. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

The New Educational Review

Eva Hejnová

Rhoyroy Eyas

Educational Studies in Mathematics

Procedia - Social and Behavioral Sciences

Daniela Caprioara

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Skills 360 – Problem-Solving 1: Developing the Right Skills

Welcome back to the Business English Skills 360 podcast as we look at solving problems. In this lesson, we’ll focus on the skills you need to solve problems.

Problems are an unavoidable part of life and work, and in business, your ability to solve them is critical. Fortunately, problem-solving skills can be developed.

The first key skill is analytical thinking. Instead of trying to come up with any old solution, take time to thoroughly understand the problem. What caused it? Can it be broken down into parts? Analyzing a problem requires critical thinking, which helps you understand connections, prioritize, and identify patterns.

Next is creativity, which involves looking at the problem from different perspectives and asking open-ended questions. Creativity, paired with analytical skills, leads to innovative solutions, as it helps you break free from conventional thinking. However, trial and error is often part of the process, and that’s where resilience comes in. Resilience is the ability to stay focused and calm when problems get tough. It’s about persevering, even when your first idea doesn’t work.

Collaboration is another crucial skill, as problem-solving often involves working with others. Effective communication and emotional intelligence are vital in navigating complex group dynamics, especially when tensions are high. Finally, decisiveness is essential to avoid getting stuck in “analysis paralysis.” Making decisions, even with limited information, is crucial for moving forward.

Developing these skills – analysis, creativity, resilience, collaboration, and decisiveness – will significantly enhance your problem-solving abilities.

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Can AI Match Human Ingenuity in Creative Problem-Solving?

When ChatGPT and other large language models began entering the mainstream two years ago, it quickly became apparent the technology could excel at certain business functions, yet it was less clear how well artificial intelligence could handle more creative tasks.

Sure, generative AI can summarize the content of an article, identify patterns in data, and produce derivative work—say, a song in the style of Taylor Swift or a poem in the mood of Langston Hughes—but can the technology develop truly innovative ideas?

Specifically, Harvard Business School Assistant Professor Jacqueline Ng Lane was determined to find out “how AI handled open-ended problems that haven’t been solved yet—the kind where you need diverse expertise and perspectives to make progress.”

In a working paper published in the journal Organization Science , Lane and colleagues compare ChatGPT’s creative potential to crowdsourced innovations produced by people. Ultimately, the researchers found that both humans and AI have their strengths—people contribute more novel suggestions while AI creates more practical solutions—yet some of the most promising ideas are the ones people and machines develop together.

Lane cowrote the paper with Léonard Bouissioux, assistant professor at the University of Washington’s Foster School of Business; Miaomiao Zhang, an HBS doctoral student, Karim Lakhani, the Dorothy & Michael Hintze Professor of Business Administration at HBS; and Vladimir Jacimovic, CEO and founder of ContinuumLab.ai and executive fellow at HBS.

Crowdsourcing people for ‘moonshots’

Any innovation process usually starts with brainstorming, says Lane, whose research has long looked at how creative ideas are produced.

“You start with defining the problem, then you generate ideas, then you evaluate them and choose which ones to implement.”

“It’s like a funnel,” she says. “You start with defining the problem, then you generate ideas, then you evaluate them and choose which ones to implement.”

Research has shown that crowdsourcing can be an effective way to generate initial ideas. However, the approach can be time-consuming and expensive. Creative teams typically offer incentives to respondents for their ideas. Then teams often must wait for input and then comb through ideas to come up with the most promising leads.

An off-the-shelf large language model such as ChatGPT, however, is free or low cost for end users, and can generate an infinite number of ideas quickly, Lane says. But are the ideas any good?

To find out, Lane and her fellow researchers asked people to come up with business ideas for the sustainable circular economy, in which products are reused or recycled to make new products. They disseminated a request on an online platform, offering $10 for participating and $1,000 for the best idea. Here’s part of their request:

We would like you to submit your circular economy idea, which can be a unique new idea or an existent idea that is used in the industry.

Here is an example: Car sharing in order to reduce the carbon footprint associated with driving. …

Submit your real-life use cases on how companies can implement the circular economy in their businesses. New ideas are also welcome, even if they are “moonshots.”

Seeking creative ideas from ChatGPT

The researchers asked for ideas that would involve “sharing, leasing, reusing, repairing, refurbishing [or] recycling existing materials and products as long as possible.” Suggestions would be scored for uniqueness, environmental benefits, profit potential, and feasibility.

Some 125 people replied with contributions, offering insights from a variety of industries and professional backgrounds. One, for example, proposed a dynamic pricing algorithm for supermarkets to cut down on food waste, while another suggested a mobile app that could store receipts to reduce paper waste.

At the same time, the research team employed prompt engineering techniques to craft a variety of AI prompts. Using these carefully designed prompts, they generated several hundred additional solutions through ChatGPT. The team strategically modified their prompts to:

• Challenge the model to create more ideas.
• Mimic the perspective of someone from a particular industry, job title, and place—a persona.
• Remind the model to provide ideas that reflect the scoring criteria.

The team then recruited some 300 evaluators well-versed in the circular economy to evaluate a randomized selection of the ideas based on the scoring criteria.

People are creative, but AI ideas are more feasible

The evaluators judged the human solutions as more novel, employing more unique “out of the box” thinking. However, they found the AI-generated ideas to be more valuable and feasible.

For example, one participant from Africa proposed creating interlocking bricks using foundry dust and waste plastic, creating a new construction material and cutting down on air pollution at the same time. “The evaluators said, ‘Wow, this is really innovative, but it would never work,’” Lane says.

“We were surprised at how powerful these technologies were.”

One ChatGPT response, meanwhile, created an idea to convert food waste into biogas, a renewable energy source that could be used for electricity and fertilizer. Not the most novel idea, the researchers noted, but one that could be implemented and might show a clear financial return.

“We were surprised at how powerful these technologies were,” Lane says, “especially in these early stages in the creative process.”

How to reach the best solutions

The “best” ideas, Lane says, may come from those in which humans and AI collaborate, with people engineering prompts and continually working with AI to develop more original ideas.

“We consistently achieved higher quality results when AI would come up with an idea and then we had an instruction that said: Make sure before you create your next idea, it’s different from all the ones before it,” Lane explains.

Additional prompts increased the novelty of the ideas, generating everything from waste-eating African flies to beverage containers tracked by smart chips that instantly pay consumers for recycling them.

Based on the findings, the researchers suggest business leaders keep a few points in mind when implementing AI to develop creative solutions:

• Knowing how to ask the right questions is important. Organizations might want to invest in cultivating an “AI-literate” workforce that can understand the capabilities and limitations of AI to generate the most successful ideas.
• Organizations should resist the temptation to rely excessively on AI. That could “dumb down” the overall level of creative output over time, leading to more incremental improvements than radical breakthroughs, the team says.
• People should view generative AI models as collaborative tools. In a sequential approach, humans could brainstorm solutions, then submit them to AI to refine them and increase their value and feasibility. Alternatively, humans could work more iteratively with AI, constantly shaping and improving the ideas it provides.

The most productive way to use generative AI, the research suggests, is to combine the novelty that people excel at with the practicality of the machine. Says Lane, “We still need to put our minds toward being forward-looking and envisioning new things as we are guiding the outputs of AI to create the best solutions.”

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Enhancing students’ problem-solving skills through context-based learning

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• National Taiwan Normal University
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What is a nonverbal learning disorder? Tim Walz’s son Gus’ condition, explained

Gus Walz stole the show Wednesday when his father, Minnesota Gov. Tim Walz, officially accepted the vice presidential nomination on the third night of the Democratic National Convention. 

The 17-year-old stood up during his father’s speech and said, “That’s my dad,” later adding, “I love you, Dad.”

The governor and his wife, Gwen Walz, revealed in a People interview that their son was diagnosed with nonverbal learning disability as a teenager.

A 2020 study estimated that as many as 2.9 million children and adolescents in North America have nonverbal learning disability, or NVLD, which affects a person’s spatial-visual skills.

The number of people who receive a diagnosis is likely much smaller than those living with the disability, said Santhosh Girirajan, the T. Ming Chu professor of biochemistry and molecular biology and professor of genomics at Penn State.

“These individuals are very intelligent and articulate well verbally, but they are typically clumsy with motor and spatial coordination,” he told NBC News. “It’s called a learning disorder because there are a lot of cues other than verbal cues that are necessary for us to keep information in our memory.”

People with NVLD often struggle with visual-spatial skills, such as reading a map, following directions, identifying mathematical patterns, remembering how to navigate spaces or fitting blocks together. Social situations can also be difficult. 

“Body language and some of the things we think about with day-to-day social norms, they may not be able to catch those,” Girirajan said. 

Unlike other learning disabilities such as dyslexia, signs of the disability typically don't become apparent until adolescence. 

Early in elementary school, learning is focused largely on memorization — learning words or performing straightforward mathematical equations, at which people with NVLD typically excel. Social skills are also more concrete, such as playing a game of tag at recess. 

“But as you get older, there’s a lot more subtlety, like sarcasm, that you have to understand in social interactions, that these kids might not understand,” said Laura Phillips, senior director and senior neuropsychologist of the Learning and Development Center at the Child Mind Institute, a nonprofit organization in New York.

In her own practice, she typically sees adolescents with NVLD, who usually have an average or above average IQ, when school demands more integrated knowledge and executive functioning, such as reading comprehension or integrating learning between subjects. They also usually seek help for something else, usually anxiety or depression, which are common among people with NVLD. 

Sometimes misdiagnosed as autism

Amy Margolis, director of the Environment, Brain, and Behavior Lab at Columbia University, is part of a group of researchers that is beginning to call the disability “developmental visual-spatial disorder” in an effort to better describe how it affects people who have it.

People with NVLD are “very much verbal,” Margolis said, contrary to what the name suggests.

The learning disability is sometimes misdiagnosed as autism spectrum disorder. Margolis led a 2019 study that found that although kids with autism spectrum disorder and NVLD often have overlapping traits, the underlying neurobiology — that is, what’s happening in their brains to cause these traits — is unique between the two conditions.

Margolis is trying to get NVLD recognized by the DSM-5, the handbook health care providers use to diagnose mental health conditions. Without such official recognition, people with NVLD can struggle to get the resources they need, such as special class placements or extra support in school.

“Without an officially recognized diagnosis, it’s hard for parents to understand how to seek information, and then communicate to other people what kinds of things might be challenging for their kid,” Phillips said, adding that widespread awareness is key to helping these families navigate NVLD.

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.