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Problem Solving Style Inventory

Do you engage employees to help fix company problems, or do you usually devise resolutions on your own? The Problem-Solving Style Inventory (PSSI) evaluates your typical problem-solving techniques against the four critical factors to consider when solving specific situations. Help supervisors, managers, and team leaders identify their dominant and supportive styles of decision-making and conflict resolution within their work environment using this effective training tool.

Problem-Solving Style: How It Works

The Problem-Solving Style Inventory , self and feedback forms, provide 30 pairs of statements describing how people typically solve problems or make decisions. Scoring the participants' selections allows everyone to generate an overall problem-solving technique and decision-making style preference profile. One's sub-scores indicate the usage of each of the five problem-solving styles.

The Five Problem-Solving Styles:

  • Ego-oriented
  • We-oriented
  • Other-oriented

Combining the self and feedback scores allows managers to compare their data with their team's responses. Participants learn about the different problem-solving styles, the four critical factors in choosing a style, analyze the possible overuse or underuse of each style, and design personal action plans. The self-inventory may be completed either before training or at the session. The feedback inventory should be completed and returned before the session so they can be scored and summarized.

Problem-Solving Style:  Uses and Applications

The Problem-Solving Style Inventory illustrates the various styles available to a supervisor or manager for solving problems and making decisions. You can plot a manager's problem-solving behavior along two axes, the first being "ego-centered behavior," or the extent to which a manager attempts to solve all problems by themselves or with little help. Meanwhile, "other-centered behavior" is how a manager includes other people in the problem-solving or decision-making process.

Learning Outcomes

By the end of this workshop, you will be able to:

  • Identify which styles you tend to use and ignore
  • Understand when and how to use different styles
  • Determine which of the five styles is most useful for your team
  • Identify the essential factors to consider when choosing a style

Product Details

Product Type: Assessment

Target Audience: Leadership teams and upper management Measures: A manager's preference for one of five problem-solving styles

Dimensions: Communication, problem-solving, team-building, and conflict resolution Time Required: Scoring: 10 minutes. Interpretation: one hour.

What to Order

Facilitator Guide: Order One Guide Per Trainer.

The Facilitator Guide includes background information, administrative guidelines, and a step-by-step workshop outline.

Paper Assessment 5-Pack: Order One Pack for Up to Five Participants.

The Paper Assessment is ideal for facilitators who prefer to oversee scoring and administration of the assessment. It includes pressure-sensitive forms for manual scoring.

Observer Form 5-Pack: Order One Pack for Up to Fiver Participants.

The Observer Form is designed to check the accuracy of a coach’s perception by allowing them to see how their peers perceive their style.

About the Author

Kenneth R. Phillips, Ph.D. , is the president of Phillips Associates, a performance management and sales performance training and consulting firm. He has been helping large and small organizations achieve improved performance since 1975. A noted authority in the performance management and sales performance training arenas, Dr. Phillips is a frequent speaker for numerous regional and local ASTD and SHRM groups. He held administrative positions with two national corporations and two colleges before pursuing his Ph.D. in organizational behavior at Northwestern University. Phillips Associates has a reputation as a supplier of programs and services that make a measurable impact on productivity.

Problem-Solving Style Questionnaire

Aamir Ranjha

October 23, 2023

Problem-Solving Style Questionnaire

Table of Contents

Here in this post, we are sharing the  “Problem-Solving Style Questionnaire”.  You can read psychometric and Author information.  We have thousands of Scales and questionnaires in our collection ( See Scales and Questionnaires ). You can demand us any scale and questionnaires related to psychology through our community , and we will provide you with a short time. Keep visiting  Psychology Roots .

Author Details

Translation availability, background/description, administration, scoring and interpretation, reliability and validity, available versions, important link, scale file:, frequently asked questions, help us improve this article, share with us, about problem-solving style questionnaire.

Thomas Cassidy and Christopher Long Parker

(I still confuse between above two. Some Resources mention 1st one and some on second one. Hope as researcher you can help to verify this)

Problem-Solving Style Questionnaire

The Problem-Solving Style Questionnaire (PSSQ) is a self -report questionnaire that measures four dimensions of problem-solving style: sensing, intuitive, feeling, and thinking. It was developed by Thomas Cassidy and Christopher Long in 1996, based on Carl Jung ’s theory of psychological types.

The PSSQ is a widely used instrument for assessing problem-solving style in a variety of settings, including educational institutions, workplaces, and counseling offices. It has been shown to be a reliable and valid measure of problem-solving style, and it has been used in numerous research studies to investigate the relationship between problem-solving style and other variables, such as academic achievement , job performance, and mental health.

The PSSQ is a relatively short instrument, consisting of 20 items with five items for each dimension. Respondents rate each item on a five-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree). The scores for each dimension are then summed to create a total score for that dimension.

The following is a brief interpretation of the four PSSQ dimensions:

  • Sensing: People with a high sensing problem-solving style prefer to learn through concrete experiences and focus on practical details. They may also be good at hands-on tasks and troubleshooting problems.
  • Intuitive: People with a high intuitive problem-solving style prefer to learn through abstract concepts and theories and focus on the big picture. They may also be good at thinking creatively and coming up with new ideas.
  • Feeling: People with a high feeling problem-solving style prefer to make decisions based on personal values and emotions. They may also be good at empathizing with others and considering their needs .
  • Thinking: People with a high thinking problem-solving style prefer to make decisions based on logic and objective reasoning. They may also be good at analyzing information and identifying patterns and trends.

It is important to note that everyone has a unique combination of problem-solving styles. There is no one “best” problem-solving style. The best way to solve a problem is to use the style that is most effective for the specific situation.

The PSSQ can be used in a variety of ways. It can be used to help individuals understand their own problem-solving style and to develop strategies for improving their problem-solving skills. It can also be used to identify individuals who may need additional support in problem-solving.

For example, a student who has a high intuitive problem-solving style may benefit from learning how to break down complex problems into smaller steps. A student who has a high sensing problem-solving style may benefit from learning how to see the big picture and generate ideas.

The PSSQ can also be used in the workplace to help employees understand their own problem-solving style and to develop teams with a variety of problem-solving styles. This can lead to more effective problem-solving and better decision-making.

The Problem-Solving Style Questionnaire (PSSQ) can be administered in a variety of settings, including educational institutions, workplaces, and counseling offices. It is a relatively short instrument, so it can be administered individually or in groups.

To administer the PSSQ, simply provide respondents with a copy of the questionnaire and instruct them to read each item carefully and rate it on a five-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree). Once respondents have completed the questionnaire, collect the questionnaires and score them.

To score the PSSQ, simply sum the responses to the five items for each dimension. The total score for each dimension ranges from 5 to 25. Higher scores indicate a stronger preference for that problem-solving style.

Here are some additional tips for administering the PSSQ:

  • Provide respondents with a quiet and comfortable place to complete the questionnaire.
  • Allow respondents enough time to complete the questionnaire without feeling rushed.
  • Be sure to answer any questions that respondents may have about the questionnaire.
  • Once respondents have completed the questionnaire, thank them for their time.

The Problem-Solving Style Questionnaire (PSSQ) has been shown to be a reliable and valid measure of problem-solving style.

Reliability refers to the consistency of a measure. A reliable measure is one that produces similar results when administered to the same people at different times.

Validity refers to the accuracy of a measure. A valid measure is one that measures what it is intended to measure.

The PSSQ has been shown to be reliable in a number of studies. For example, Cassidy and Long (1996) reported that the PSSQ had a Cronbach’s alpha of .77, which is considered to be a good level of reliability.

The PSSQ has also been shown to be valid in a number of studies. For example, Cassidy and Long (1996) found that the PSSQ scores were correlated with other measures of problem-solving style, such as the Myers-Briggs Type Indicator (MBTI).

In addition, the PSSQ has been used in a number of research studies to investigate the relationship between problem-solving style and other variables, such as academic achievement , job performance, and mental health. The results of these studies suggest that the PSSQ is a valid measure of problem-solving style.

Ghodrati, M., Bavandian, L., Moghaddam, M. M., & Attaran, A. (2014). On the relationship between problem-solving trait and the performance on C-test.  Theory and practice in language studies ,  4 (5), 1093-1100.

Khan, M. J., Younas, T., & Ashraf, S. (2016). Problem Solving Styles as Predictor of Life Satisfaction Among University Students.  Pakistan Journal of Psychological Research ,  31 (1).

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What is the Problem-Solving Style Questionnaire (PSSQ)? The PSSQ is a self -report questionnaire that measures four dimensions of problem-solving style: sensing, intuitive, feeling, and thinking.

What are the four dimensions of problem-solving style measured by the PSSQ? The four dimensions of problem-solving style measured by the PSSQ are: Sensing, Intuitive, Feeling, and Thinking.

How is the Problem-Solving Style Questionnaire scored? The PSSQ is scored by summing the responses to the five items for each dimension. The total score for each dimension ranges from 5 to 25. Higher scores indicate a stronger preference for that problem-solving style.

What are the benefits of using the Problem-Solving Style Questionnaire? The PSSQ can be used to help individuals and teams understand their problem-solving styles and develop strategies for improving their problem-solving skills. It can also be used to identify individuals who may need additional support in problem-solving.

Is the PSSQ a reliable and valid measure of problem-solving style? Yes, the PSSQ has been shown to be a reliable and valid measure of problem-solving style. It has been used in a number of research studies to investigate the relationship between problem-solving style and other variables, such as academic achievement , job performance, and mental health.

Please note that Psychology Roots does not have the right to grant permission for the use of any psychological scales or assessments listed on its website. To use any scale or assessment, you must obtain permission directly from the author or translator of the tool. Psychology Roots provides information about various tools and their administration procedures, but it is your responsibility to obtain proper permissions before using any scale or assessment. If you need further information about an author’s contact details, please submit a query to the Psychology Roots team.

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If you have any scale or any material related to psychology kindly share it with us at  [email protected] . We help others on behalf of you.

problem solving style indicator

I am a senior clinical psychologist with over 11years of experience in the field. I am the founder of Psychology Roots, a platform that provides solutions and support to learners and professionals in psychology. My goal is to help people understand and improve their mental health, and to empower them to live happier and healthier lives.

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Self-Assessment • 20 min read

How Good Is Your Problem Solving?

Use a systematic approach..

By the Mind Tools Content Team

problem solving style indicator

Good problem solving skills are fundamentally important if you're going to be successful in your career.

But problems are something that we don't particularly like.

They're time-consuming.

They muscle their way into already packed schedules.

They force us to think about an uncertain future.

And they never seem to go away!

That's why, when faced with problems, most of us try to eliminate them as quickly as possible. But have you ever chosen the easiest or most obvious solution – and then realized that you have entirely missed a much better solution? Or have you found yourself fixing just the symptoms of a problem, only for the situation to get much worse?

To be an effective problem-solver, you need to be systematic and logical in your approach. This quiz helps you assess your current approach to problem solving. By improving this, you'll make better overall decisions. And as you increase your confidence with solving problems, you'll be less likely to rush to the first solution – which may not necessarily be the best one.

Once you've completed the quiz, we'll direct you to tools and resources that can help you make the most of your problem-solving skills.

How Good Are You at Solving Problems?

Instructions.

For each statement, click the button in the column that best describes you. Please answer questions as you actually are (rather than how you think you should be), and don't worry if some questions seem to score in the 'wrong direction'. When you are finished, please click the 'Calculate My Total' button at the bottom of the test.

Answering these questions should have helped you recognize the key steps associated with effective problem solving.

This quiz is based on Dr Min Basadur's Simplexity Thinking problem-solving model. This eight-step process follows the circular pattern shown below, within which current problems are solved and new problems are identified on an ongoing basis. This assessment has not been validated and is intended for illustrative purposes only.

Below, we outline the tools and strategies you can use for each stage of the problem-solving process. Enjoy exploring these stages!

Step 1: Find the Problem (Questions 7, 12)

Some problems are very obvious, however others are not so easily identified. As part of an effective problem-solving process, you need to look actively for problems – even when things seem to be running fine. Proactive problem solving helps you avoid emergencies and allows you to be calm and in control when issues arise.

These techniques can help you do this:

PEST Analysis helps you pick up changes to your environment that you should be paying attention to. Make sure too that you're watching changes in customer needs and market dynamics, and that you're monitoring trends that are relevant to your industry.

Risk Analysis helps you identify significant business risks.

Failure Modes and Effects Analysis helps you identify possible points of failure in your business process, so that you can fix these before problems arise.

After Action Reviews help you scan recent performance to identify things that can be done better in the future.

Where you have several problems to solve, our articles on Prioritization and Pareto Analysis help you think about which ones you should focus on first.

Step 2: Find the Facts (Questions 10, 14)

After identifying a potential problem, you need information. What factors contribute to the problem? Who is involved with it? What solutions have been tried before? What do others think about the problem?

If you move forward to find a solution too quickly, you risk relying on imperfect information that's based on assumptions and limited perspectives, so make sure that you research the problem thoroughly.

Step 3: Define the Problem (Questions 3, 9)

Now that you understand the problem, define it clearly and completely. Writing a clear problem definition forces you to establish specific boundaries for the problem. This keeps the scope from growing too large, and it helps you stay focused on the main issues.

A great tool to use at this stage is CATWOE . With this process, you analyze potential problems by looking at them from six perspectives, those of its Customers; Actors (people within the organization); the Transformation, or business process; the World-view, or top-down view of what's going on; the Owner; and the wider organizational Environment. By looking at a situation from these perspectives, you can open your mind and come to a much sharper and more comprehensive definition of the problem.

Cause and Effect Analysis is another good tool to use here, as it helps you think about the many different factors that can contribute to a problem. This helps you separate the symptoms of a problem from its fundamental causes.

Step 4: Find Ideas (Questions 4, 13)

With a clear problem definition, start generating ideas for a solution. The key here is to be flexible in the way you approach a problem. You want to be able to see it from as many perspectives as possible. Looking for patterns or common elements in different parts of the problem can sometimes help. You can also use metaphors and analogies to help analyze the problem, discover similarities to other issues, and think of solutions based on those similarities.

Traditional brainstorming and reverse brainstorming are very useful here. By taking the time to generate a range of creative solutions to the problem, you'll significantly increase the likelihood that you'll find the best possible solution, not just a semi-adequate one. Where appropriate, involve people with different viewpoints to expand the volume of ideas generated.

Tip: Don't evaluate your ideas until step 5. If you do, this will limit your creativity at too early a stage.

Step 5: Select and Evaluate (Questions 6, 15)

After finding ideas, you'll have many options that must be evaluated. It's tempting at this stage to charge in and start discarding ideas immediately. However, if you do this without first determining the criteria for a good solution, you risk rejecting an alternative that has real potential.

Decide what elements are needed for a realistic and practical solution, and think about the criteria you'll use to choose between potential solutions.

Paired Comparison Analysis , Decision Matrix Analysis and Risk Analysis are useful techniques here, as are many of the specialist resources available within our Decision-Making section . Enjoy exploring these!

Step 6: Plan (Questions 1, 16)

You might think that choosing a solution is the end of a problem-solving process. In fact, it's simply the start of the next phase in problem solving: implementation. This involves lots of planning and preparation. If you haven't already developed a full Risk Analysis in the evaluation phase, do so now. It's important to know what to be prepared for as you begin to roll out your proposed solution.

The type of planning that you need to do depends on the size of the implementation project that you need to set up. For small projects, all you'll often need are Action Plans that outline who will do what, when, and how. Larger projects need more sophisticated approaches – you'll find out more about these in the article What is Project Management? And for projects that affect many other people, you'll need to think about Change Management as well.

Here, it can be useful to conduct an Impact Analysis to help you identify potential resistance as well as alert you to problems you may not have anticipated. Force Field Analysis will also help you uncover the various pressures for and against your proposed solution. Once you've done the detailed planning, it can also be useful at this stage to make a final Go/No-Go Decision , making sure that it's actually worth going ahead with the selected option.

Step 7: Sell the Idea (Questions 5, 8)

As part of the planning process, you must convince other stakeholders that your solution is the best one. You'll likely meet with resistance, so before you try to “sell” your idea, make sure you've considered all the consequences.

As you begin communicating your plan, listen to what people say, and make changes as necessary. The better the overall solution meets everyone's needs, the greater its positive impact will be! For more tips on selling your idea, read our article on Creating a Value Proposition and use our Sell Your Idea Skillbook.

Step 8: Act (Questions 2, 11)

Finally, once you've convinced your key stakeholders that your proposed solution is worth running with, you can move on to the implementation stage. This is the exciting and rewarding part of problem solving, which makes the whole process seem worthwhile.

This action stage is an end, but it's also a beginning: once you've completed your implementation, it's time to move into the next cycle of problem solving by returning to the scanning stage. By doing this, you'll continue improving your organization as you move into the future.

Problem solving is an exceptionally important workplace skill.

Being a competent and confident problem solver will create many opportunities for you. By using a well-developed model like Simplexity Thinking for solving problems, you can approach the process systematically, and be comfortable that the decisions you make are solid.

Given the unpredictable nature of problems, it's very reassuring to know that, by following a structured plan, you've done everything you can to resolve the problem to the best of your ability.

This assessment has not been validated and is intended for illustrative purposes only. It is just one of many Mind Tool quizzes that can help you to evaluate your abilities in a wide range of important career skills.

If you want to reproduce this quiz, you can purchase downloadable copies in our Store .

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Afkar Hashmi

😇 This tool is very useful for me.

about 1 year

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Subscribe to our Creative Problem Solving Online Learning Course today!

About problem solving styles.

problem solving style indicator

Knowledge of style is important in education in a number of ways. It contributes to adults’ ability to work together effectively in teams and in large groups.  It provides information that helps educators understand their own personal strengths and how to put them to work as effectively as possible across many tasks and challenges. It helps educators communicate more effectively with each other, but also with parents, community members, and, of course, with students.  In addition to its importance for adults, style can also be important in designing and differentiating instruction.

The VIEW Model

Our approach to problem solving style (the VIEW model) represents and assesses three dimensions and six specific styles that are unique and important in understanding and guiding the efforts of individuals and groups to manage their creative problem solving and change management as effectively as possible.

Orientation to Change

The first VIEW dimension involves your preferences in two general styles for managing change and solving problems creatively. We identify this as “Orientation to Change;” its two contrasting styles are the “Explorer” and the “Developer.” Explorers thrive on and seek out novelty and original ideas (“thinking out of the box”), and they may find externally imposed procedures and structures confining and limiting to their imagination and energy. Developers are concerned with practical applications and the reality of the task, and they use their creative and critical thinking in ways that are clearly recognized by others as being helpful and valuable. They’re good at finding workable possibilities and guiding them to successful implementation. They are creative in “thinking better inside the box.”

Manner of Processing

The second dimension of VIEW, Manner of Processing describes the person’s preference for working externally (i.e., with other people throughout the process) or internally (i.e., thinking and working alone before sharing ideas with others) when managing change and solving problems.

Ways of Deciding

The third dimension of VIEW describes the major emphasis the person gives to people (i.e., maintaining harmony and interpersonal relationships) or to tasks (i.e., emphasizing logical, rational, and appropriate decisions) when making decisions during problem solving or when managing change.

Through our research and development efforts, our instrument, VIEW: An Assessment of Problem Solving Style, translates the VIEW model of style into measurable dimensions. The VIEW assessment is a practical and useful tool for anyone who wishes to understand his or her own approach to change or problem solving. Contact us for more information about the VIEW instrument.

Practical Applications of VIEW

Understanding problem solving styles can be helpful in many ways to individuals, teams, small groups, and organizations. VIEW: An Assessment of Problem Solving Style is a carefully researched, but simple and easy-to-use tool that can enable people to understand their style preferences and to use that knowledge in many powerful ways. 

These pages illustrate briefly a variety of practical applications of the VIEW assessment that cut across many settings or contexts (including, for example: large, global organizations; smaller business and professional settings; educational institutions, hospitals, religious organizations, arts organizations, or other non-profits). 

VIEW can be a valuable tool for individuals who are concerned with understanding their personal style preferences and improving their problem solving effectiveness, for teams or groups who need to work together successfully, and to organizations in their efforts to build a constructive work climate, to recognize and value diversity, and to manage change for long-term success. 

Click on any of the following ten applications of VIEW to see examples for that area. (You may also click here to Download a PDF file with all the applications examples in one file.)

  • Improving Problem Solving
  • Communicating Effectively
  • Enhancing Personal Productivity
  • Providing and Receiving Feedback
  • Facilitating Groups
  • Managing Change
  • Developing Leadership
  • Designing Instruction
  • Building Teams
  • Coaching and Mentoring

Free Resources

Click here to find a number of free resources that will explain CPS, to obtain articles that deal with both research and practice, and to obtain an extensive bibliography to give you direction for future reading and study.

Online Resources:

Click here for advanced online resources in PDF format that deal with applications of the VIEW Problem Solving Style model. These resources are available at a reasonable cost for immediate download. The cost of each one includes permission to duplicate the file for up to three other individuals at no additional charge.

Distance Learning Resources:

Click here for information about our extensive (newly revised and updated) distance learning modules on CPS.

Print Resources:

We also have print publications about problem-solving style that you can purchase.  Click here to view those publications.

Workshops, Training, Consulting Services:

Our CPS programs and services are custom-tailored to meet your needs and interests. We will confer with you, create a complete proposal to meet your unique needs, and work closely with you to carry out our collaborative plan. Click here for more.

We believe that all people have strengths and talents that are important to recognize, develop, and use throughout life.  Read more.

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Problem Solving Style

This 30-item instrument allows individuals to gain insight on their dominant and supportive styles of solving problems and making decisions in their work units or teams as well as receive feedback from others.

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  • Understand which problem-solving and decision-making style one is predisposed to use or ignore
  • Determine whether one’s use of the 5 styles is appropriate for one’s work groups or teams
  • Identify the important factors to consider when choosing a style to solve a problem or make a decision
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Theory Problem Solving Style Inventory is based on the Problem Solving Styles Model. This model illustrates the various styles available to a supervisor or manager for solving problems and making decisions. A manager’s problem-solving or decision-making behavior can be plotted along 2 axes:

Ego-Centered Behavior : The extent to which a manager attempts to solve all problems or make all decisions by him/herself with little or no input from others. Other-Centered Behavior : The extent to which a manager includes other people in the problem-solving or decision-making process.

The degree to which a manager uses these 2 behaviors to solve problems and make decisions gives rise to the 5 styles shown in the model. All 5 styles are useful managerial approaches to solving problems and making decisions in certain situations. How It Works The inventory presents 30 pairs of statements that describe how people go about solving problems and making decisions. Individuals choose the statement that is most characteristic of their approach. By scoring and charting results, participants generate an overall Problem-Solving/Decision-Making Style Preference Profile, with sub scores indicating one’s usage level of each of the 5 styles. Feedback scores provide comparison data. Participants learn about the styles, the 4 key factors in choosing a style, analyze the possible overuse or underuse of each style, and make action plans. Uses for the Problem Solving Style Inventory The Problem Solving Style Inventory assessment and Feedback Forms are effective when used together as a stand-alone tool as well as part of a larger program. The Problem Solving Style Inventory can be used in a variety of ways, including:

  • As part of a basic supervisory or management training program
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What to Order/Product Contents Order one Facilitator Guide per trainer and one Participant Guide per participant. To provide individuals with feedback, order one Feedback Form for up to 8 of the participant’s employees, peers, or managers. (We recommend ordering at least 3 Feedback Forms per participant.)

Facilitator Guide includes:

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  • Overhead transparency masters
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Participant Guide includes:

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Cognitive style: The role of personality and need for cognition in younger and older adults

  • Published: 03 August 2019
  • Volume 40 , pages 4460–4467, ( 2021 )

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  • Andrea Vranic   ORCID: orcid.org/0000-0002-4235-8014 1 ,
  • Blaz Rebernjak 1 &
  • Marina Martincevic 1  

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Cognitive style seems to influence cognitive activities in many important ways. A recently proposed Cognitive style indicator (CoSI) operationalizes three cognitive styles: knowing, planning and creating style. This study was designed to investigate the relation of five factor personality traits and Need for Cognition (NFC) with regard to a preference towards a certain cognitive style, depending on the age of participants. A sample of students ( n  = 108) and middle-aged employed adults ( n  = 115) completed CoSI, Rational-Experiential Inventory (REI-10) and Ten Item Personality Inventory (TIPI). The results of exploratory and confirmatory factor analysis have validated CoSI on an independent sample and confirmed its originally proposed 3-factor structure. Furthermore, the mediation model with multigroup structure for two age cohorts highlighted several significant connections between personality traits, NFC and three hypothesized cognitive styles. Results suggest that the relations between personality traits and cognitive style differ in different age groups, and are partially or totally mediated by NFC.

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Vranic, A., Rebernjak, B. & Martincevic, M. Cognitive style: The role of personality and need for cognition in younger and older adults. Curr Psychol 40 , 4460–4467 (2021). https://doi.org/10.1007/s12144-019-00388-6

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4 Problem Solving Styles and How to Sell to Each (video)

Clarifying, ideating, developing, and implementing are different problem-solving styles. Thus, in this Expert Insight Interview, Sarah Thurber discusses how to understand and interact with each of the four problem-solving styles in sales. Sarah Thurber is a Managing Partner at FourSight, an international writer, and a thought leader on creative thinking and problem-solving.

The interview discusses:

Implementing.

Clarifying the situation is the foundation of any problem-solving. It narrows it down and enables you to take the next step in the right direction. When dealing with people who are clarifiers, salespeople need to ask as many clarifying questions as possible. Buyers nowadays are overwhelmed with the selection of products and services on the market. Research shows that having 20 different jams offered to buyers is too overwhelming for them to make a purchase. Thus, the salesperson’s value can drastically increase if he manages to clarify the buyers’ needs.

People who prefer the ideation process are creative, visionary, and they want something unique with a story behind it. For example, if deciding which car to buy, these people would like to hear some fascinating story about the car to choose it. Thus, salespeople need to help buyers to create an emotional connection with the product. Forming a big picture and emotional connection is the key selling point in this case because these buyers are passionate and intuitive rather than analytical.

Developers are very analytical and want to be sure that they made the right decision. These people are perfectionists and very detail-oriented. Salespeople should offer a couple of alternatives to buyers to see what differentiates their choice from the other ones. Developers need time to think, and this can be challenging for the sales process. Thus, salespeople must find a perfect balance between being patient with buyers and giving them a sense of urgency to decide to prevent them from becoming no-decision buyers.

Most of the business people, especially salespeople, are implementors wanting to find the solution yesterday. Research shows that implementors like to work only with other implementors because they get each other’s sense of urgency. However, when dealing with implementors, it is crucial to make sure that they go through all previous creative problem-solving processes because, if not, they can get a buyer’s remorse later. Hence, salespeople’s job is to slow implementors’ point of urgency and make them go through clarifying, ideating, and developing stages. The too quick and easy sales process can cause a terrible post-sales experience.

John  is the Amazon bestselling author of Winning the Battle for Sales: Lessons on Closing Every Deal from the World’s Greatest Military Victories and Social Upheaval: How to Win at Social Selling. A globally acknowledged Sales & Marketing thought leader, speaker, and strategist. He is CSMO at Pipeliner CRM. In his spare time, John is an avid Martial Artist.

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Mathematical Problem-Solving Style and Performance Of Students

Mathematical Problem-Solving Style and Performance Of Students

  • Luvie Jhun S. Gahi
  • Ronald E. Almagro
  • Richie Ryan C. Sudoy
  • Dec 21, 2023

Mathematical Problem-Solving Style and Performance of Students

1 Luvie Jhun S. Gahi, 2 Ronald E. Almagro, 3 Richie Ryan C. Sudoy

1 Student, Master of Arts in Education Major in Mathematics, St. Mary’s College of Tagum,

2 Student, Master of Arts in Education Major in English, St. Mary’s College of Tagum,

3 Student, Doctor of Philosophy in Educational Management, Davao del Norte State College, Philippines

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7011142

Received: 10 October 2023; Revised: 17 November 2023; Accepted: 20 November 2023; Published: 21 December 2023

This study aimed to determine whether the mathematical problem-solving style significantly affects the students’ performance in which a descriptive-correlational research design was used. Through stratified sampling, there were 291 first-year college respondents in the local college in Sto. Tomas, Davao del Norte who were chosen. This study used one adapted questionnaire and one researchers-made questionnaire with Mean, Pearson r, Standard Deviation, T-test, and Analysis of Variance used as the statistical tools. The students’ mathematics attained very good performance with the mathematical problem-solving style of students’ sensing, intuition, feeling, and thinking moderately observed. The findings revealed that the mathematical problem-solving style has a significant relationship with Students’ performance. However, there is a significant difference in the mathematical problem-solving style of students when grouped according to various programs. The result also revealed that there is no significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female).  Students, instructors, college administrators, and Commission of Higher Education officials (CHED) are encouraged to value the importance of mathematical problem-solving style in the performance of the students. Instructors, college administrators, and CHED officials must establish programs that will enhance the mathematical problem-solving styles and performance of students. College instructors and administrators must work collaboratively to achieve better performance in the mathematics. Therefore, these should ensure that the necessary materials, resources, activities, and differentiated instruction are provided and used to meet the students’ needs to learn and to encourage in the problem-solving style.

Keywords: Student’s Profile, Mathematical Problem-Solving Style, and Performance of Students

INTRODUCTION

Good problem-solving abilities are required for all issues emerging from daily activity or to progress through the developmental stages. Effective problem-solving style has been linked to beneficial psychological outcomes such as competence, productivity, and optimism (Carver & Scheier, 1999; Chang & D’Zurilla, 1996; Elliott, et al., 1994). Additionally, according to the National Council of Teachers of Mathematics (NCTM), problem-solving ability is an essential component of all mathematics learning. The ability to solve problems can provide significant benefits in everyday life and the workplace. However, problem-solving is not only a goal of learning mathematics, but also a major method of learning mathematical concepts.

Likewise, the process of problem-solving begins with the observation of a gap, the application, and the complete evaluation of a theory to close that loophole. Styles of problem-solving are viewed as contrasting individuals’ unique characteristics with the behaviors that people prefer to draw and concentrate on their efforts to arrive at some comprehension or awareness, generate ideas, and make plans for the work(Sutherland, 2002).In the local college of Sto. Tomas, Davao del Norte, the varying levels of student performance in mathematics courses was used in this study. Surprisingly, there seemed to be a gap in the published research addressing this issue (Almagro etal., 2023)

The ability and style to solve problems increase the students’ comfort level when solving mathematical problems and practical difficulties. In turn, having the ability to solve problems has several advantages. For instance, problem-solving style is a feature of mathematical activity and a key method for developing mathematical understanding (NCTM, 2000). This statement implies that problem-solving style is an essential component of mathematics education. Furthermore, students learn to apply their mathematical skills in various ways; they gain a deeper understanding of mathematical concepts and gain firsthand experience as a mathematician by solving problems (Badger et al., 2012). Consequently, instruction should be advanced to enable the students to recognize and address the issues they encountered in real-life situations (Phonapichat et al., 2014). Nevertheless, several research findings suggest that children struggle to solve problems because of (Herawatty et al., 2018). Thus, learning mathematics should encourage students to solve problems confidently using mathematics. Learning mathematics in school should assist the students in understanding and applying mathematics to their problems that occur in their daily lives and in the workplace. The learning program must enable students to develop new mathematical knowledge through problem-solving style, solve mathematics and other problems, implement and adjust various problem-solving strategies, and monitor and reflect on the problem-solving style (NCTM, 2000).

The existing literature acknowledges challenges in mathematical problem-solving, but there is a significant gap in understanding the specific difficulties students face and the effectiveness of different problem-solving strategies. GanzonandEdig (2022), recognize the challenges, there is a need for in-depth investigations into the categories of difficulties encountered during the problem-solving process, low academic performance during in the pandemic. Additionally, the literature notes the importance of problem-solving models (Foshay & Kirkley, 2003; Almagro & Edig,2023), but a gap exists in understanding the comparative effectiveness of these motivated learning strategies. Moreover, within Realistic Mathematics Education (RME), recognized for its real-world emphasis, there is a need to examine the specific contextual factors that contribute to or hinder students’ success in mathematical problem-solving. Addressing these gaps will provide valuable insights for supporting students in developing effective problem-solving skills in mathematics.

The objective of this study is to investigate the relationship between mathematical problem-solving styles and the performance of students. Specifically, it aims to identify the various problem-solving styles employed by students, explore the challenges they face in mathematical problem-solving, and assess the impact of different problem-solving strategies on overall performance. The study seeks to contribute valuable insights that can inform educational practices and enhance students’ proficiency in mathematical problem-solving.

Statement of the Problem

The purpose of this study was to determine the relationship between mathematical problem-solving style and performance of first-year college students in Sto. Tomas College of Agriculture, Science and Technology (STCAST)in the academic year 2022-2023.

Specifically, these research questions sought to answer the following:

1. What is the measurable level of students’ performance in solving math problems? 2. What is the quantifiable level of students’ mathematical problem-solving style, considering the dimensions of sensing, intuition, feeling, and thinking? 3. Is there a significant and measurable relationship between students’ mathematical problem-solving style and their performance? 4. Can the mathematical problem-solving style of students be significantly differentiated when grouped according to sex and program, making it a specific and measurable analysis? 5. What specific and measurable instructional interventions can be proposed based on the study’s results, ensuring relevance and time-bound applicability?

The following hypotheses was tested at a 0.05 level of significance. Specifically, this was drawn to determine whether mathematical problem-solving style of students differ in terms of their sex and program.

  • There is no significant relationship between the mathematical problem-solving style and performance of students.
  • There is no significant difference on the mathematical problem-solving style of the students when classified according to sex and programs.

Theoretical Framework

The study is grounded in the original problem-solving style model, rooted in the concept of psychological functions as proposed by Jung (1923) and further developed by Moon (2008) and Taylor & Mackenny (2008). This model encompasses thinking, feeling, sensation, and intuition as the four psychological functions (Ghodrati et al., 2014). Building upon this foundation, the research draws attention to gender-specific problem-solving tendencies, with Burkey and Miller (2005) finding that women often employ intuition in work settings, contrasting the emphasis on rational problem-solving linked to masculinity by Wang, Heppner, and Berry (2007). Additionally, Conner (2000) identifies women as more intuitive global thinkers, emphasizing simultaneous, interconnected processing of information. The study aligns with the belief that students’ problem-solving methods significantly influence their academic achievement and success (Poshtiban, 2007; Morton, 2001).

Conceptual Framework

The study’s conceptual paradigm, which is shown in Figure 1, summarizes the variables which composed of mathematical problem-solving style and performance of students. On the one hand, the independent variable consists of mathematical problem-solving style which includes the indicators of sensing, intuitive, feeling, and thinking. On the other hand, the dependent variable consists of the performance of students in mathematics which composed of the moderating variable, i.e., the student respondent’s profile which are classified into sex and program. The researchers’ interpretation explains what the study wants to achieve as emphasized in the figure below. As such, the study aims to assess the Mathematical Problem-Solving Style of College students, and its relationship to their performance, and produce Students’ Instructional Intervention Plan that will improve their styles in solving mathematical problems.

problem solving style indicator

 Figure 1. The Conceptual Paradigm of the Study

METHODOLOGY

This section covers the study’s numerous methodologies, which include the research design, respondents, research instrument, data gathering procedures, statistical treatment of data, and ethical considerations.

Research Design

This study employed descriptive and correlational study design. Descriptive research entails gatherings of quantitative data that may be tabulated along a scale in numerical forms, such as test scores. It entails collecting data by describing occurrences and then arranging, tabulating, displaying, and summarizing the data (Glass & Hopkins, 1984). The researcher will utilize this design to determine and describe the variables employed in this study. It utilized the mean test since this aimed to measure the level of performance of students.

Correlational study, meanwhile, tried to establish correlations between two or more variables. It looked to see if a rise or drop in one variable corresponded to an increase or decrease in another (Tan, 2014). This design will be utilized by the researcher to examine and determine the existing correlations between the variables in this research.

This study was concerned with data collection utilizing adopted research instrument and a pilot-tested researchers’ made examination to evaluate the hypotheses whether the mathematical-problem solving style influences the student performance. It will test the data using the proper statistical tools. Furthermore, the study’s major objective is to distinguish between the mathematical problem-solving styles of students when they are classified by sex and program. Thus, the study intends to look into the relationship between mathematical problem-solving style and performance of freshmen students in various programs at Sto. Tomas College of Agriculture, Sciences, and Technology.

Participants of the Study

The respondents of this research were the first-year college students enrolled in bachelor of Technical and Vocational Teacher Education (BTVTED), Bachelor of Science in Agricultural Business (BSAB), Bachelor of Science in Office Administration (BSOA), and Bachelor of Public Administration (BPA)programs for the school year 2022-2023. The respondents’ total population size of this study comprises of 1,188 students coming from four (4) programs in Sto. Tomas College of Agriculture, Sciences and Technology (STCAST). Specifically, BSOA department consists of 426 first-year students, BSAB department consists of 381 first-year students, BPA department consists of 217 first-year students, and BTVTED department consists of 164 first-year students. By using Qualtrics online sample size calculator, given the identified collective population size of 1,188 students, the ideal sample size of this quantitative study will consist of 291 students in total.

Moreover, this study utilized a stratified sampling technique to determine the sample size and determine the final total number of respondents. As a result, the BSOA program has an ideal sample of 176 students, BSAB program with 94 students, BPA with 53 students, and BTVTED with 40 students.

Materials/Research Instrument

One adapted research instrument and one researcher-made examination were used in this study. This was selected and modified to match the overall objectives of the study. These research instruments were validated by a panel of experts.

Problem-Solving Style Questionnaire (PSSQ). This instrument contains a 20-item survey questionnaire comprising the six (4) components problem-solving in mathematics such as Sensing (5 items), Intuitive (5 items), Feeling (5 items), and Thinking (5 items). This questionnaire was anchored on a 5-point Likert scale ranging from 5 as strongly agree to 1 as strongly disagree.

The following parameter limits, with its corresponding descriptions, were applied for the level of students’ mathematical problem-solving style.

The instrument for performance of students in Mathematics was a pilot-tested researcher-made questionnaire worth 40-item questionnaire. This instrument had been determined to possess good psychometric validity and reliability. The value of Cronbach’s a for the total scale is 0.747. All items of problem-solving skills are acceptable.

The percentage of the test score was computed by dividing the number of correct responses over the total highest possible score by multiplying it by 70 add 30. The highest probable score to achieve will be 40.

For the level of the mathematical problem-solving skills, the following parameter was used.

Data Gathering Procedure

The necessary data was gathered in a systematic procedure, which will involve the following.

Seeking permission to conduct the study. The researcher sought approval to conduct the research project. Primarily, the researcher will acquire a letter of recommendation from the College President of Santo Tomas College of Agriculture, Sciences, and Technology (STCAST). After acceptance, the researcher submitted a copy of the recommendation to the respective Department Heads of the following four (4) Degree Programs such as Bachelor of Science in Agriculture and Business (BSAB), Bachelor of Technical Vocational Teacher Education (BTVTED), Bachelor of Science in Office Administration (BSOA), and Bachelor of Public Administration (BPA) to finalize the approval to conduct the entire study.

General orientation and seeking of consent from research respondents . The study’s conduct was to regulate by ethical values, i.e., respect for individuals, beneficence, and justice, particularly in terms of data privacy and protection. Prior to data collection, the researcher will generate informed consent or assent forms and request them from respondents by e-mail. As evidence of their voluntary involvement in the full study, all forms will be delivered and signed electronically by those research respondents through e-mail message.

In addition, the researcher gave a brief 30-minute virtual presentation about the findings to the respondents. In accordance to this procedure, respondents who confirmed their voluntary involvement in the study were given a unique connection to Google Classroom developed by the researcher where the participants can partake in a brief virtual presentation. This was done specifically before conducting the survey. However, those respondents who were unable to attend the orientation due to unforeseen circumstances or personal reasons were educated about the research by phone call or chat through Facebook Messenger by the researcher. In addition, all respondents received a recorded video from the virtual presentation, which can be observed within the Google Classroom built by the researcher for the research.

Administration and retrieval of the questionnaire . The study took place in February of the school year 2022-2023. In order to carry out the research, the researcher will first develop Google Forms that will be utilized to collect responses from respondents based on the survey questions from the questionnaires. The quantitative data for this study was collected online using Google Forms. The researcher managed all direct contact and administration of surveys to respondents.

All surveys were allotted within one 90-minute session commencing with the Mathematical Problem-Style Questionnaire (MPSQ) and researcher-made examination. To protect the data, respondents was required to take the survey at a location and on a technological device (e.g., laptop, cellphone, or tablet) where only they have access to those offered online survey surveys via Google Forms. This was explicitly stated in their Informed Consent Form (ICF). In addition, the data questionnaire was returned to the researcher on time. Furthermore, the researcher handled personal communication and questionnaire administration. Finally, questionnaires were administered when the respondents’ 90-minute session has expired.

Checking, collating, and processing of data. The researcher gathered, validated, and quantified the respondents’ scores collected in an Excel file throughout this step. Following the tabulation, the data were submitted to an expert or certified statistician for data analysis. The researcher analyzed the results based on the data analysis for specific discoveries, discussions, and conclusions. This was accomplished mostly through data table and graphical presentations. Furthermore, descriptive statements were used to further explain and easily grasp the findings in relation to the study’s variables.

Statistical Tool for Data Analysis

The study’s findings were examined and comprehended properly using statistical methods such as Mean, Standard Deviation, Pearson r, T-test, and Analysis of Variance (ANOVA).

Mean . This method of analysis was used to measure the level of performance of students and their mathematical problem-solving style. Specifically, this was addressed in the first and second research questions.

Standard Deviation . A standard deviation is a statistical measure of the dispersion of a dataset in reference to its mean. This kind of analysis was used to determine how widely scattered the data is or how close the scores are to the mean. This was specifically answer the first and second research questions.

Pearson r. This statistical analysis was utilized to establish the existence of a significant relationship between mathematical problem-solving style and the performance of students. This will be utilized to specifically address the third research question.

T-test. This statistical analysis was utilized to determine if there was significant difference in the mathematical problem-solving style of students when classified into sex. This will specifically answer the fourth research question.

Analysis of Variance . This statistical analysis was utilized to determine if there was a significant difference in the mathematical problem-solving style of students when classified into four (4) different programs.

RESULTS AND DISCUSSION

In this chapter, the researchers present the results and discussions from the data gathered. In particular, this shows the data in tables and its corresponding descriptive interpretations.

Level of Mathematical Problem-Solving Style of Students in terms of Sensing

Table 1 presents the level of Mathematical Problem-Solving Style of Students in terms of Sensing. The item “As a student, I like to solve math problems and I am comfortable to trying to learn new skills.” has the highest mean of 3.69 with a descriptive equivalent of high. This is followed by the item “Before I put energy into solving math problems, I want to know first the benefits I can get from it.”, with a mean of 3.62 and high descriptive equivalent. On the contrary, the item “I tend to focus on immediate problems and let others worry about the distant future.” with the lowest mean of 3.20 and descriptive equivalent of moderate.

Table 1 Level of Mathematical Problem-Solving Style of Students in terms of Sensing

Furthermore, it has a category mean of 3.42 with descriptive equivalent of high. This indicates that the mathematical problem-solving style of students in terms of sensing is observed. Moreover, it has an Standard Deviation (SD) of 0.97.

The dispersion of the mathematical problem-solving style of students in terms of sensing based on the answers of the students revealed that the SD is 0.97. This indicates that the measures of variability of sensing as a mathematical problem-solving style of students are near the mean.

The result shows that students are interested in solving math problems and they are comfortable in trying new skills. It is also much observed that before solving math problems, students want to identify first the benefits they can get from it. Furthermore, students focus on immediate problems. Moreover, Vicente et al. (2002) observed that individuals with a sensation-type problem-solving style tend to focus on details and gather specific, factual data from their environment using their five senses. This approach involves a preference for concrete, practical, and tangible information, rather than abstract or theoretical concepts. These individuals tend to use a step-by-step approach to problem-solving, relying on established rules and procedures, and often prefer to work with real-world problems that have clear and immediate applications. Similarly, the study of Hsieh and Lin (2006) investigated the connection between mathematical problem-solving style and sensory preference among high school students. The study established that students with a sensing preference tended to use a more practical, sequential, and concrete problem-solving approach.

Level of Mathematical Problem-Solving Style of Students in terms of Intuitive

Table 2 presents the level of Mathematical Problem-Solving Style of Students in terms of Intuitive. The item “ As a student, I solve math problems accurately by knowing all the details of the problem.” has the highest mean of 3.68 with a descriptive equivalent of high. This is followed by the item “As a student, I enjoy solving mathematical problems.” with a mean of 3.36 and a descriptive equivalent of moderate. On the contrary, the item “ As a student, I solve mathematical problems quickly without wasting a lot of time on details.” has a mean of 3.05 with a descriptive equivalent of moderate.

Table 2 Level of Mathematical Problem-Solving Style of Students in terms of Intuitive

Furthermore, it has a category mean of 3.26 with a descriptive equivalent of moderate. It means that the mathematical problem-solving style of students in terms of intuitive is moderately observed. Moreover, the standard deviation of 1.01 in the category mean indicates that the measures of the variability of the mathematical problem-solving style of students in terms of intuition are near the mean.

It is observed that the students solve math problems accurately by knowing all the details of the problem. Additionally, it is moderately observed that students enjoy solving mathematical problems quickly and without wasting a lot of time on details. Hafriani (2018) suggests that students rely heavily on their intuition when it comes to solving mathematical problems. Students who use intuitive thinking to solve mathematical problems exhibit several characteristics: directness, self-evidence, intrinsic certainty, perseverance, coercion, extrapolation, globality, and implicitness.

Similarly, in the study conducted by Wuryanieet al., (2020) they found that students tend to rely on intuition when solving problems, exhibiting traits such as directness, self-evidence, extrapolation, intrinsic certainty, coercion, and decisiveness.

Level of Mathematical Problem-Solving Style of Students in terms of Feeling

Table 3 presents the level of Mathematical Problem-Solving Style of students in terms of feeling. The item “I want to solve math problems within a group and not individually.” has the highest mean of 3.73 with a descriptive equivalent of high. This is followed by the item “As a student, I can tell how others feel about solving math problems.” with a mean of 3.60 and a descriptive equivalent of high. On the other hand, the item “I try to please others and need occasional praise for myself.” has the lowest mean of 3.05 with a descriptive equivalent of moderate.

Table 3 Level of Mathematical Problem-Solving Style of Students in terms of Feeling

Moreover, it has a category mean of 3.48 with a descriptive equivalent of high. It implies that the mathematical problem-solving style of students in terms of feeling is observed. Consequently, the standard deviation of 0.98 in the category mean indicates that the measures of variability of the mathematical problem-solving style of students in terms of feeling are close to the mean.

Based on the results, it is observed that students want to solve math problems within a group and not individually. In addition, it is also observed that students can tell how others feel about solving math problems. It is moderately observed that in solving math problems, students try to please others and need occasional praise for their selves. A study by Goez et al. (2005) found that students must gain information and abilities related to feelings. Moreover, Altun (2003) examined the students who tend to rely on their feelings when solving problems prioritize their emotional and personal approaches in the problem-solving process. Along with this, Ahmed et al. (2014) stated that the mathematical problem-solving style has a favorable effect on the student’s attention, motivation to learn, choice of learning tools, self-regulation of learning, and academic performance.

Level of Mathematical Problem-Solving Style of students in terms of Thinking

Table 4 presents the level of mathematical problem-solving style of students in terms of thinking. The item “ As a student, I don’t let mathematics word problems discourage me, no matter how difficult they are.” has the highest mean of 3.65 with a descriptive equivalent of high. This is followed by the item “I solve math problems by analyzing all the facts and putting them in systematic order.” with a mean of 3.56 with a descriptive equivalent of high. On other hand, the item “ When I have a math problem to be solved, I solve it, even if others’ feelings might get hurt in the process.” has the lowest mean of 3.08 with a descriptive equivalent of moderate.

Furthermore, it has a category mean of 3.36 with a descriptive equivalent of moderate. This implies that the mathematical problem-solving style of students in terms of intuitive is moderately observed. Consequently, the standard deviation of 1.01 in the category means indicates that the measures of variability of the mathematical problem-solving style of students in terms of intuition are close to the mean.

Table 4 Level of Mathematical Problem-Solving Style of Students in terms of Thinking

Based on the results, it is observed that students approach mathematics word problems in a thoughtful and analytical manner. They are not easily discouraged by the difficulty of the problems and instead persevere until they find a solution. Students employ a systematic approach to problem-solving, carefully considering all the relevant facts and putting them in a logical order. This approach aligns with the findings of Khan et al. (2016), who emphasize the importance of analysis and research in effective problem-solving. Additionally, students demonstrate a commitment to objectivity and impartiality, seeking solutions that are grounded in evidence and reason. This aligns with the notion that mathematical problem-solving requires a high level of reflective thinking, as highlighted by Kneeland (2001) and Macaso and Dagohoy (2022). Moreover, the results of this study suggest that students are generally well-equipped to tackle mathematics word problems. They possess the necessary skills and mindset to approach these problems in a thoughtful, analytical, and objective manner. Thinking based on a mental process is an essential component of problem-solving solving, and problem-solving abilities are dependent on the correct application of thinking and solution processes. Furthermore, high-level thinking skills are involved in the intricate process of problem-solving (Gürsan & Yazgan, 2020).

Summary of the Level of Mathematical-Problem Solving Style of Students

Table summarizes the level of mathematical problem-solving style of students. Among the four indicators, “feeling” acquire the highest mean of 3.48 with descriptive equivalent of high. “Sensing” developed a mean of 3.42 with a descriptive equivalent of high. They have an SD of 0.98 and 0.97, respectively. It is followed by “thinking” with a mean of 3.36 with a descriptive equivalent of moderate and an SD of 1.01. On other hand, “intuitive” got the lowest mean of 3.26 with a descriptive equivalent of moderate and an SD of 1.01.

Table 5 Summary of the Level of Mathematical-Problem Solving Style of Students

Furthermore, it has an overall mean of 3.38 with a descriptive equivalent of moderate. This means that the mathematical problem-solving style of students is moderately observed. The findings suggest that there is a high degree of homogeneity in the mathematical problem-solving styles of the students, as evidenced by the small standard deviation of 0.99 in the overall mean. This indicates that the measures of variability in the students’ responses are clustered closely around the mean. Such a narrow range of variability in the problem-solving styles implies that the students have similar levels of proficiency in this variable.

Particularly, the results suggest that “feeling and sensing” as students’ mathematical problem-solving style is observed. This means that students want to solve math problems within a group and they like to solve math problems and are comfortable trying to learn new things. Moreover, “thinking and intuitive” as students’ mathematical problem-solving style is moderately observed. This indicates that students solve math problems by analyzing all the facts and knowing all the details of the problem.

This finding is supported by a recent study by TIMSS and PISA, which found that students can use their mathematical understanding and knowledge to solve problems (IEA, 2016). PISA assesses students’ ability to use their knowledge and skills in recognizing, analyzing, and solving problems in a variety of situations(OECD, 2019). Moreover, the result confirms the findings of Schoenfeld (2013), who mentioned that the mental state of students is an essential aspect of learning mathematics. The belief system of the student regarding himself, mathematics, and problem-solving determines student progress in problem-solving. This is also agreed by Hendriana et al., (2017) who mentioned that one of the fundamental mathematical abilities that students who study mathematics must develop is the ability to solve problems.

Level of Performance of Students in Solving Math Problems

Table 6 depicts the level of performance of students in solving math problems. The level of performance of students in terms of answering the 40-item Mathematics test has a mean of 60.84 with a descriptive equivalent of above average. This indicates that the performance of students in mathematics is very good.  Furthermore, it has an SD of 10.48. This demonstrates whether one’s scores on mathematical problem-solving style were extremely high or extremely low. This suggests that students’ capacity to solve mathematical problems is more likely to deviate from the mean.

The result shows that the students were able to select and correctly identify the appropriate answer to the given questions. They could choose the correct equation from the problem and accurately determine the answer in the problem scenario. Additionally, they were able to verify their responses by selecting the correct answer to the given problem.

Table 6 Level of Performance of Students in Solving Math Problems

As cited by Heris and Sumarmo (2014), problem-solving style are basic mathematical skills students need to learn. Fernandez et al. (2017) also added that problem-solving is a significant part of learning, making it of particular importance for the study of mathematics. Furthermore, another component of learning mathematics is problem-solving. This means that students need to become proficient in a variety of problem-solving style in mathematics in order to improve their creativity, reasoning, critique, and systematic thinking (NCTM, 2000). Therefore, completing mathematical problems is a crucial component of the learning objectives that must be encountered (Surya et al., 2017).

Significance of the Relationship Between the Variables

Table 7 presents the relationship between Mathematical Problem-Solving Style and the Performance of Students in Mathematics.

The correlation of Mathematical Problem-Solving Style has a significant relationship with the Performance of Students in Mathematics (p<0.05) with a coefficient determination of 0.733. Specifically, there is a strong positive correlation between the variables, and the p-value of the two variables is less than the 0.05 level of significance, which indicates that there is a significant relationship between the mathematical problem-solving style and the performance of students in mathematics (r=0.733,p=0.000. Thus, the null hypothesis is rejected.

Since the result confirmed that the mathematical problem-solving style and performance of students have a very high relationship, this means that the mathematical problem-solving style of the students significantly affects their performance in math. It can be seen from the aforementioned discussion that the mathematical problem-solving style of students is an important factor that affects their performance.

Table 7 Significance of the Relationship Between the Variables

It can be deduced that if students’ mathematical problem-solving styles will be developed, then it enhances their performance in school. This is supported by the study conducted by Suratno et al., (2020) which found that there is a significant positive correlation between students’ problem-solving styles and academic performance of students. Additionally, Mustafić et al. (2017) established that a student would perform exceptionally well in any science subject if they propose a high level of self-concept toward problem-solving skills.

The significant difference in the Mathematical Problem-solving Style of students when grouped according to sex

To determine if there was a significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female), a t-test was used. Table 8 shows the result.

Using the t-test, the obtained t-value is -2.211 and the resulting p-value is 0.9860. This result indicates that the difference in mathematical problem-solving style between males and females is not statistically significant at the 0.05 level. Therefore, the researchers fail to reject the null hypothesis that there is no significant difference in mathematical problem-solving style between male and female students.

Table 8 The significant difference in mathematical-problem solving style of students when grouped according to sex

This means that students’ mathematical problem-solving style when grouped according to sex (male and female) does not vary. This finding is consistent with previous research conducted by Rusdiet al., (2020) who mentioned that men and women have similar mathematical problem-solving styles. Male and female students may understand the information, describe their knowledge, and ask relevant questions. They can use notations, symbols, and mathematical models to describe the problem and solution properly.  Moreover, Goos et al. (2017) studies examined the impact of gender differences on students’ mathematical learning outcomes have yielded inconsistent results. While some studies have demonstrated differences between genders, indicating that either men or women perform better, other studies have found no significant gender differences.

The significant difference in mathematical problem-solving style of students when grouped according to Program

Table 9 presents the significant difference in the mathematical problem-solving style of students when grouped according to four different programs: BTVTED, BSAB, BSOA, and BPA.

The result shows that there is a positive significant difference in the mathematical problem-solving style of students when grouped according to different programs since the f-value is 12.46 and the p-value is 0.000 which is lesser than 0.05 alpha level of significance. It indicates the mathematical problem-solving style of students from BTVTED, BSAB, BSOA, and BPA is significantly different. Therefore, the null hypothesis is rejected.

Table 9 The significant difference in mathematical problem-solving style of students when grouped according to Program.

The result means that students coming from four programs have different problem-solving styles in mathematics. This finding is concurrent to the first model of problem-solving styles based on the concept of psychological functions (Jung, 1923; Moon, 2008; Taylor & Mackenny, 2008). This model consists of four psychological functions as thinking, feeling, sensation, and intuition (Ghodrati et al., 2014). As such, Hacısalihlioğlu et al. (2003)stated that achieving success in problem-solving is linked to possessing abilities such as critical thinking, decision-making, reflective thinking, inquiring, analyzing, and synthesizing. This is further confirmed by Wen-Chun et al. (2015) that students use different problem-solving styles in solving math problems.

SUMMARY, CONCLUSION, AND RECOMMENDATIONS

This chapter presents the summary of the major findings of the study, the conclusion, and the proposed recommendations for possible implementations.

Summary of Findings

The major findings of the study are the following:

  • For the level of performance of students in solving math problem, the level of performance of students in terms of answering the 40-item Mathematics test has a mean of 60.84 with a descriptive equivalent of high. This indicates that the performance of students in mathematics is very good. Furthermore, it has an SD of 10.48. This demonstrates whether one’s scores on mathematical problem-solving style were extremely high or extremely low.
  • For the level of mathematical problem-solving style, “feeling” got the highest mean of 3.48 with an SD of 0.98. This is followed by “sensing” with a mean of 3.42 and an SD of 0.97. Both indicators got a similar descriptive equivalent of high. On other hand, “intuitive” got the lowest mean of 3.26 and an SD of 1.01 with a descriptive equivalent of moderate. Furthermore, it has an overall mean of 3.38 and an SD of 0.99 with a descriptive equivalent of moderate.
  • The statistical analysis shows that there is a strong positive correlation (r=0.733) between the mathematical problem-solving style and students’ performance. The p-value of the two variables is less than 0.05, indicating that the correlation is statistically significant. This means that there is a significant relationship between the students’ mathematical problem-solving style and their academic performance. As a result, the null hypothesis is rejected.
  • The statistical analysis shows that there is no significant difference (t=211, p=0.9860) in the mathematical problem-solving style of students when grouped according to sex (male and female). The result indicates that the difference in mathematical problem-solving style between males and females is not statistically significant at the 0.05 level. Therefore, the researchers fail to reject the null hypothesis that there is no significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female). Meanwhile, there is a significant difference (f=12.46, p=0.000) in the mathematical problem-solving style of students when grouped in accordance to different programs since the f-value is 12.46 and the p-value is 0.000 which is lesser than 0.05 alpha level of significance. This indicates the mathematical problem-solving style of students from BTVTED, BSAB, BSOA, and BPA is significantly different. Therefore, the null hypothesis is rejected.
  • Based on the results of the study, here are the instructional intervention plan that can be proposed:
  • For thinking and intuitive problem solvers – Provide explicit instruction on how to justify mathematical solutions using evidence and logical reasoning. Encourage intuitive problem-solvers to verbalize their thought process and explain how they arrived at their solution. Moreover, provide opportunities for students to develop their critical thinking skills through activities such as puzzles, brain teasers, and logic games. Encourage students to explain their reasoning and thought processes when solving problems.
  • For sensing and feeling problem solvers – Provide opportunities for collaborative problem-solvers to work in pairs or small groups to solve problems. Encourage the students to take turns explaining their thought process and to ask questions to deepen their understanding. Provide opportunities for independent problem-solving and self-directed learning while allowing for collaborative work. Furthermore, provide support and guidance as needed to help students build their problem-solving skills. Gradually remove support as students become more confident and independent.

The findings from the study led the researcher to draw the following conclusions:

  • Performance of students in solving math problems is very good.
  • Mathematical problem-solving style of students is moderately observed.
  • There is a significant relationship between mathematical problem-solving style and the performance of students.
  • There is no significant difference in the mathematical problem-solving style of students when grouped according to sex. However, students’ mathematical problem-solving style is significantly different when grouped according to different programs.
  • Understanding students’ different mathematical problem-solving styles can help instructors tailor their instruction to meet the needs of different learners and create a more inclusive classroom environment.

Recommendations

Based on the findings, analysis, and conclusion drawn in this study, the following recommendations were summarized:

  • Students are encouraged to learn effectively and independently in solving mathematical tasks. They may discover that strengthening their different mathematical problem-solving style would boost their performance in math. This can be achieved by helping them discover their different problem-solving styles and identifying strategies that can assist them in solving mathematical problems. By doing so, they can improve their performance in mathematics and unleash their full potential in this subject area.
  • Instructors, college administrators, and local college officials are urged to develop enrichment activities to help their students in developing their mathematical problem-solving styles. This is especially true when it comes to encouraging students to take the initiative, establish their own mathematical problem-solving strategy, set learning goals, and assess their abilities to specify the sources they need to learn, particularly in mathematics. Teachers may create engaging instructional intervention programs to deepen students’ interests in problem-solving in Mathematics. Furthermore, they can engage in and be creative with technology, mentoring, and coaching students who are having difficulty completing mathematical problems.
  • The current study’s findings emphasize the relevance of problem-solving style in mathematics and provide ways to apply and improve it. The establishment of a curriculum and instructional strategy for applying it to students is required so that teachers and students can continue and maximize their mathematical problem-solving style. To ensure that these tactics are embedded in students, ongoing efforts will be required. To maximize students’ problem-solving style in Mathematics, STCAST instructors and administrators should work collaboratively to achieve the objective. They should ensure that the necessary materials, resources, activities, and differentiated instruction are available and used to meet students’ needs to be motivated in learning.
  • Future research for developing other intervention programs needed to identify the factors that might improve the mathematical problem-solving style to enhance students’ performance.

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Problem Solving Styles in the Inventive Process

problem solving style indicator

The Use of Mental and Visual Models

Donald R. Loftin December 2, 2007

INTRODUCTION This paper provides a summary of the research that was completed [Loftin, 2006] in partial fulfillment of a Masters of Engineering in System Engineering at Penn State Great Valley (PSGV). The research explored the relationships between the problem solving styles of inventors, their inventive processes, and their inventions. In particular, the research attempted to link and build upon Kirton’s Adaption-Innovation (A-I) Theory [Kirton, 1994, 2000, 2003] and invention as a Cognitive Process [Gorman & Bernard, 1990]. Gorman and Carlson suggest that invention can be seen as a cognitive process in which mental, visual and mechanical models are aligned in order to solve some perceived problem. This process is illustrated in the Figure below. The research explored whether preferences were shown in the use of these models by inventors with different problem solving styles.

problem solving style indicator

DEFINITION OF TERMS Analogy – A form of logical inference, or an instance of it, based on the assumption that if two things are known to be alike in some respects, they may be alike in other respects. For this research: the use of a general pattern of a solution in one domain to create the general pattern of a solution in another domain.

Mental Model – The structures of inventions that are formed and manipulated mentally without the use of any external visual or mechanical representations. This includes the mental manipulation of proposed changes to an existing invention.

Visual Model – The structures of inventions that are formed and manipulated through the use of pictures, drawings, and other representations on paper. This term also refers to other 2-dimensional views of a structure that might be presented on a computer screen.

Mechanical Model – The structures of inventions that are formed and manipulated through the use of 3-dimensional physical structures, including samples, miniature representations, mock-ups, and prototypes. This term also refers to 3-dimensional modeling of an invention through the use of Computer-Aided-Design (CAD) applications.

Inventor – An individual who, individually or as part of a team, creates an item for which a patent is applied or for which a trade-secret is created.

Heuristics – This refers to techniques or rules of thumb that may be used within the inventive process.

Intuition – The apparent ability to acquire knowledge without a clear inference or reasoning process. (Within this research, the gathering of information about the problem followed by the appearance in the mind of the solution after “sleeping” on it.)

Top Down Problem Solving – A problem solving approach in which the system is first formulated, specifying but not detailing any first-level subsystems. Each subsystem is then refined in yet greater detail, sometimes in many additional subsystem levels, until the entire specification is reduced to base elements.

Bottom Up Problem Solving – A problem solving approach in which the individual base elements of the system are first specified in great detail. These elements are then linked together to form larger subsystems, which then in turn are linked, sometimes in many levels, until a complete top-level system is formed.

SUMMARY OF RESEARCH The research was conducted by interviewing 12 different inventors. At the start of the interviews, each inventor was given the KAI. The KAI scores and sub-scores of each inventor are shown below in order of most innovative to most adaptive. Although none of the subscale scores are invalid, the ones highlighted in red below have some variation that is subject to interpretation. Note that in each case, the SO subscale score is higher and the E subscale score is lower. Since these individuals are inventors, this deviation is not completely unexpected, i.e., both the generation of ideas and the attention to detail are essential skills.

problem solving style indicator

The interview process itself was conducted by asking each inventor to respond to 10 questions. Each session was recorded for further analysis. The questions consisted of both quantitative (3, 5, 7) questions and qualitative (1, 2, 4, 6, 8, 9, 10) questions. The questions that were used for the interview are provided below. 1. In your own words, please describe the typical process you follow when inventing. 2. What role do mental models play in your inventive process? What role do visual models play in your inventive process? What role do mechanical models play in your inventive process? 3. On a scale from zero to ten, please identify the level of importance of mental models, visual models, and mechanical models, respectively, in your inventive process. 4. Please describe your typical inventive process in terms of the sequence of use of the three types of models. 5. What percentage of the time do you typically spend during the inventive process with each of these models? 6. Are there specific triggers or criteria that indicate the need to move from one model to another? If so, please describe these and give examples. 7. Which of the models, i.e., mental, visual or mechanical, are most difficult for you to create and/or use? Which models are the least difficult for you to create and use? 8. Pick one of your inventions and describe the process you followed for that invention, with special attention to the role of mental, visual, and mechanical models in the process. 9. Do you have any heuristics that you use regularly to solve problems you encounter during the inventive process? If so, please describe them. 10. For which of your inventions was the inventive process the most difficult and why? For which of your inventions was the inventive process the least difficult and why?

SUMMARY OF RESULTS The three quantitative results were recorded and used to perform statistical analysis. A brief explanation of each measure is provided below. 1. Relative Importance of Models – Each inventor was asked to provide the relative importance of each model in their inventive process using a number from zero to 10 with zero being of no importance and 10 being the most important. The inventors were not asked to make these numbers different. For example, one inventor rated all three models as 10. 2. Time Spent in Models – Each inventor was asked to provide the percentage of the inventive process that they spend in each model. The sum of the percentages for each model was expected to sum to 100%. In those cases in which it did not, a weighting factor was used to correct the values. 3. Difficulty of Use of Model – Each inventor was asked to order the models in terms of ease of use. A value of 1 was assigned to the easiest model and a value of 3 to the hardest model. When two models were seen to be equally easy or hard, an average between their positions was taken. For example, if the mental model was seen as easiest but the visual and mechanical models were seen as being equally difficult, the mental model was given a value of 1 and the visual and mechanical models were given the values of 2.5. The results for each inventor interviewed are shown in the table below.

problem solving style indicator

QUANTITATIVE ANALYSIS Most of the inventors interviewed did not participate significantly in the creation of the mechanical models. Instead, they often reported that they performed a consulting service or performed testing of the product. As a result, only limited inferences could be determined from these results. However, an examination of the relative importance of mechanical models for the 3 most innovative inventors show values of 6 or below while the 3 most adaptive individuals show values of 7 or above. Statistical analysis of the data did show evidence for preferences in the way that mental and visual models are used. Details of this analysis are available in the original research paper [Loftin, 2005]. The table below shows an example of another way in which this data can be analyzed.

problem solving style indicator

To analyze this data, the data was divided into three groups. Group 1 consists of the 3 most innovative inventors, Group 2 consists of the 6 inventors in the middle, and Group 3 consists of the 3 most adaptive inventors. The following observations can be made about each of these groups: • Group 1 (Most Innovative) – 7 out of 9 measures show a preference for the use of the mental model vs. the visual models with 2 of 3 inventors indicating that mental models were more important, 2 of 3 inventors indicating that they spent more time with mental models and all 3 inventors Indicating that they find mental models easier to use. • Group 2 (Middle) – 6 out of 18 measures show a preference for the use of mental models while 7 out of 18 measures show a preference for the use of visual models. There appears to be no preference within this group. • Group 3 (Most Adaptive) – 6 out of 9 measures show a preference for the use of visual models vs. mental models with 2 of 3 inventors indicating that visual models were more important, 2 of 3 inventors indicating that they spent more time with visual models and 2 of 3 inventors Indicating that they find visual models easier to use.

QUALITATIVE ANALYSIS The analysis of the data gathered from the interviews provides some additional interesting results. Apparent differences in preferred strategies for design were observed. Individuals with more innovative cognitive styles appear to start the invention process with a focus on more abstract patterns and templates that can be used to solve many problems. For example, the use of analogies to support the problem solving process was reported by three inventors, with KAI scores of 139, 120 and 119 while no other inventors mentioned this approach. Individuals with more adaptive cognitive styles appear to start the invention process with a focus on refinement and optimization of components of an overall solution. Inventors that fall between these two strategies appear to employ strategies that bring these two problem solving approaches together. They appear to accomplish this process by using intuition to bring the two strategies together into a synthesized solution in alignment with a particular problem. The use of this technique was mentioned by all inventors in the range of 100 to 120. Only one other inventor, inventor 12 with a KAI of 104, mentioned the use of this strategy. There is existing follow on research that is attempting to extract more information from the recordings that were performed for the interviews. The recordings are being transcribed on a word-for-word basis in order to facilitate the use of data mining techniques to search for meaning that may not be easily apparent since what we hear is influenced by our problem solving styles.

HYPOTHESIS The conclusion from the analysis of the data from these interviews indicates that there is evidence to support the following hypotheses which can be tested as a follow up project: 1. More innovative problem solvers have a preference for the use of mental models over visual models, spend more time with these models and find them easier to use; more adaptive problems solvers have a preference for the use of visual models over mental models, spend more time with these models and find them easier to use. 2. More adaptive problem solvers show a preference for solving problems bottom up while more innovative problem solvers show a preference for solving problems top down.

FUTURE RESEARCH One of the interesting observations that were made during this research was the grouping of inventors that reported the use of intuition to solve problems. For this sample, all 5 inventors with KAI score in the range of 108 to 120 reported the use of intuition as a primary problem solving approach. Only one other inventor (KAI of 100) reported this preference. Additional investigations is needed in order to determine whether there is a segment of the KAI continuum in which there is a strong preference for intuition while other segments on the continuum show much less preference. If so, there are several implications that could be explored. For example, one could conclude that there exists a set of problems to be solved for which the preferred technique for solving them is intuition. What then is the nature of the problems for which intuition appears to be the best tool? In addition, the most innovative inventors showed a preference for the use of analogies to solve problems while the balance of the group did not. One possible answer to be researched is whether a series of segments of the KAI continuum can be identified in which one or two preferred techniques of problem solving appear.

REFERENCES The following references are contained within the original research paper: Cheney, Margaret, [1981] 2001, Tesla: Man Out of Time, New York: Touchstone. Clapp, R. G., 1991, The fate of ideas that aim to stimulate change in a large organization, PhD Thesis, University of Hertfordshire. DeCristoforo, Danielle, 2005, Creative Style Assessment for Products of Invention, Unpublished research paper for Penn State Great Valley. Drucker, P. F., 1969, Management’s new role, Harvard Business Review. Goldman, Robert and McKenzie, John D. Jr., 2005, The Student Guide to Minitab Release 14, Pearson Education, Inc. Gorman, Michael E. and Carlson, W. Bernard, 1990, Interpreting Invention as a Cognitive Process: The Case of Alexander Graham Bell, Thomas Edison, and the Telephone, Science, Technology, & Human Values, Vol. 15, No. 2, Spring, 131-164. Huber, John C., 1998. Invention and Inventivity Is a Random, Poisson Process: A Potential Guide to Analysis of General Creativity, Creativity Research Journal, Vol. 11, No. 3, 231-241, Lawrence Erlbaum Associates, Inc. Keller, R. T. and Holland, W. E., 1978, Individual characteristics of innovativeness and communication in Research and Development organizations, Journal of Applied Psychology. Kirton, M. J., [1977, 1987] 1999, Kirton Adaption-Innovation Inventory Manual, Berkhamsted, UK: Occupational Research Centre. Kirton, M. J., [1989] 2000, Adaptors and Innovators – Styles of Creativity and Problem Solving; Hertfordshire, UK: KAI Distribution Center. Kirton, M. J., 2003, Adaption-Innovation in the Context of Diversity and Change, New York: Routledge. Kuhn, T. S., 1970, The structure of scientific revolutions, Chicago: University of Chicago Press. The original research paper is identified below: Loftin, D. R., 2006, Problem Solving Style and the Inventive Process, Penn State Great Valley

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IMAGES

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  3. Apply diverse problem-solving styles

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  4. Infographic Design Elements with Six Options for Problem Solving Steps

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  5. Indicators of the Problem Solving Process in terms of Field Dependent

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  6. Indicator of problem-solving skills instrument

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COMMENTS

  1. Problem Solving Style Inventory

    The Problem-Solving Style Inventory, self and feedback forms, provide 30 pairs of statements describing how people typically solve problems or make decisions. Scoring the participants' selections allows everyone to generate an overall problem-solving technique and decision-making style preference profile. One's sub-scores indicate the usage of ...

  2. Problem-Solving Style Questionnaire » Psychology Roots

    The Problem-Solving Style Questionnaire (PSSQ) is a self -report questionnaire that measures four dimensions of problem-solving style: sensing, intuitive, feeling, and thinking. It was developed by Thomas Cassidy and Christopher Long in 1996, based on Carl Jung 's theory of psychological types.

  3. Different Problem-Solving Styles: What Type of Problem Solver Are You

    It is classic inconsistent-type behavior to lose time dithering between all three ideas, lost in indecision. Instead, write them down in a chart. Then, give each one a score out of 5 according to its strength in whatever categories are relevant to the problem. For example, expense, time, elegance, effort.

  4. KAI

    KAI measures style of problem solving and creativity. It is available both online and as a paper form, and is used: In the training of managers and key teams as part of the management of change. In group training and individual development as part of the management of diversity. For the enhancement of group cohesion and effectiveness.

  5. A Note on the Problem Solving Style Questionnaire: An ...

    Romero et al (1992) developed a new instrument, the Problem Solving Style Questionnaire (PSSQ) also grounded in ELM, eliminating the ipsativity problem raised by Stout and Ruble (1994) and showing ...

  6. How Good Is Your Problem Solving?

    Problem solving is an exceptionally important workplace skill. Being a competent and confident problem solver will create many opportunities for you. By using a well-developed model like Simplexity Thinking for solving problems, you can approach the process systematically, and be comfortable that the decisions you make are solid. ...

  7. Understanding individual problem-solving style: A key to learning and

    Advances in our understanding of the Creative Problem Solving framework, problem-solving style, and their interactions hold great promise for future practice and research, and especially for those concerned with understanding and enhancing creative human behavior and organizational innovation (e.g., Isaksen, 2004, Isaksen and Dorval, 1993 ...

  8. PDF Problem Solving Styles

    problem you encounter in your life. The problem-solving process is a search for, and implementation of, the best possible solution for a specific problem. As a problem solver, you will develop your own method for solving problems. One of the best methods for doing this is to try to use the most effective aspects of the four different styles.

  9. About Problem Solving Styles

    Problem-solving styles are consistent individual differences in the ways people prefer to deal with new ideas, manage change, and respond effectively to complex, open-ended opportunities and challenges. Knowledge of style is important in education in a number of ways. It contributes to adults' ability to work together effectively in teams and ...

  10. Defining and Assessing Problem‐Solving Style: Design and Development of

    VIEW: An Assessment of Problem Solving Style (Selby, Treffinger, & Isaksen, 2002) is a new instrument for assessing problem-solving style, for use with individuals from ages 12 through adult. It measures three dimensions of style relating to creative problem solving and change management. In this article, we discuss the construction of the instrument, the initial evidence supporting the ...

  11. How to Coach Clients with Different Problem-Solving Styles

    Assessing a client's problem-solving style can be done with tools and frameworks such as the Kirton Adaption-Innovation Inventory, the Myers-Briggs Type Indicator, or the FourSight Thinking Profile.

  12. How to Work with Different Problem Solving Styles

    3 Communicate clearly and respectfully. The third step is to communicate clearly and respectfully with people who have different problem solving styles. This means being aware of your own ...

  13. How to Measure Your Team's Problem-Solving Skills with KPIs

    1 Define the problem. The first step in measuring your team's problem-solving skills is to clearly define the problem that needs to be solved. A well-defined problem should be specific, measurable ...

  14. 4D Training and Development Resources

    The Problem Solving Style Inventory assessment and Feedback Forms are effective when used together as a stand-alone tool as well as part of a larger program. The Problem Solving Style Inventory can be used in a variety of ways, including: As part of a basic supervisory or management training program ; As part of a leadership or team leader ...

  15. PDF Problem Solving Style Inventory Theory Dekon

    Problem Solving Style Inventory Theory Dekon

  16. Cognitive style: The role of personality and need for cognition in

    Cognitive style is a habitual individual preference in perceiving and processing information, which can influence learning, problem solving and decision making in important ways (Hayes and Allinson 1994).In the attempt to distinguish between different cognitive styles, two opposing theoretical perspectives have emerged: i) unitary (bipolar) and, ii) multidimensional approach (for review see ...

  17. 4 Problem Solving Styles and How to Sell to Each (video)

    Clarifying, ideating, developing, and implementing are different problem-solving styles. Thus, in this Expert Insight Interview, Sarah Thurber discusses how to understand and interact with each of the four problem-solving styles in sales. Sarah Thurber is a Managing Partner at FourSight, an international writer, and a thought leader on creative ...

  18. Mathematical Problem-Solving Style and Performance Of Students

    Level of Mathematical Problem-Solving Style of Students in terms of Intuitive. Table 2 presents the level of Mathematical Problem-Solving Style of Students in terms of Intuitive. The item "As a student, I solve math problems accurately by knowing all the details of the problem." has the highest mean of 3.68 with a descriptive equivalent of ...

  19. Problem Solving Styles in the Inventive Process

    Problem Solving Styles in the Inventive Process. The Use of Mental and Visual Models. Donald R. Loftin ... D. R., 2006, Problem Solving Style and the Inventive Process, Penn State Great Valley. Latest News! KAI Symposium 2024 - call for submissions. We are delighted to announce the KAI Symposium 2024 will be on 21/22 Feb 2024, and is now open ...

  20. 10.9

    Terms in this set (4) Personal Style - Sensation- Thinking. Actions Tendencies. - Emphasizes details , facts, certainty. - Is a decisive, applied thinker. - Focuses on short- term, realistic goals. - Develops rules and regulations for judging performance. Likely Occupations. - Accounting.

  21. Mathematical Problem-Solving Style and Performance of Students

    This study aimed to determine whether the mathematical problem-solving style significantly affects the students' performance in which a descriptive-correlational research design was used. Through ...

  22. Assessments

    If you are interested in gaining some new insights, or bringing an assessment program into your organization, give us a call at (888)804-COACH (2622) or (303) 838-1100. You can also email Dr. Laura at [email protected] and put "interested in assessments" in the subject line. Hogan Assessment Systems — NEW !!!

  23. Aspects and Indicators of Problem Solving

    Download Table | Aspects and Indicators of Problem Solving from publication: POLYA'S STRATEGY: AN ANALYSIS OF MATHEMATICAL PROBLEM SOLVING DIFFICULTY IN 5 TH GRADE ELEMENTARY SCHOOL | Problem ...