ERIC - Institute of Education Sciences

Chapter 1, Section 4

Solving Conceptual Problems

Not every word problem in chemistry requires calculations. Some problems ask you to apply the concepts you are studying to a new situation. In this text, these nonnumeric problems are labeled conceptual problems. To solve a conceptual problem, you still need to identify what is known and what is unknown. Most importantly, you still need to make a plan for getting from the known to the unknown. But if your answer is not a number, you do not need to check the units, make an estimate, or check your calculations.

The three-step problem-solving approach is modified for conceptual problems. The steps for solving a conceptual problem are analyze and solve. Figure 1.26 summarizes the process, and Conceptual Problem 1.1 shows how the steps work in an actual problem.

Figure 1.26 This flowchart shows the two steps used for solving a conceptual problem. Comparing And Contrasting With a conceptual problem, why is the second step called Solve rather than Calculate?

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Introduction, section snippets, references (39), cited by (15), recommended articles (6).


Psychology of Learning and Motivation

Chapter nine - conceptual problem solving in physics.

Students taking introductory physics courses focus on quantitative manipulations at the expense of learning concepts deeply and understanding how they apply to problem solving. This proclivity toward manipulating equations leads to shallow understanding and poor long-term retention. We discuss an alternative approach to physics problem solving, which we call conceptual problem solving (CPS), that highlights and emphasizes the role of conceptual knowledge in solving problems. We present studies that explored the impact of three different implementations of CPS on conceptual learning and problem solving. One was a lab-based study using a computer tool to scaffold conceptual analyses of problems. Another was a classroom-based study in a large introductory college course in which students wrote conceptual strategies prior to solving problems. The third was an implementation in high school classrooms where students identified the relevant principle, wrote a justification for why the principle could be applied, and provided a plan for executing the application of the principle (which was then used for generating the equations). In all three implementations benefits were found as measured by various conceptual and problem solving assessments. We conclude with a summary of what we have learned from the CPS approach, and offer some views on the current and future states of physics instruction.

Learning a physical science well requires not only the ability to solve quantitative problems but also to have an understanding of the concepts, their relations, and how they are used to help solve problems. In physics, instructors know, and research has documented, that students tend to focus on quantitative problem solving at the expense of learning concepts (Bagno and Eylon, 1997, Larkin, 1979; 1981b; 1983; Larkin and Reif, 1979, Tuminaro and Redish, 2007, Walsh et al., 2007). Perhaps because homework and exams in undergraduate physics courses largely demand quantitative solutions to problems, students spend time searching for and manipulating equations to get answers. This is not a bad strategy for getting good grades, but it is a poor strategy for gaining deep conceptual understanding (Kim & Pak, 2002). Without a conceptual framework that integrates and gives meaning to equations and problem solving procedures, there is very little residual learning of introductory physics several weeks after a course is over. There is a clear need to devise instructional strategies that elevate the importance of conceptual understanding, and that help students integrate conceptual knowledge with problem solving processes.

This chapter discusses several research studies exploring conceptual problem solving (CPS) in physics. Broadly speaking what we mean by CPS is integrating the solving of the problem with an analysis of the underlying concept being used. The problem solving takes place guided by the conceptual analysis. We begin by discussing the central role of problem solving in physics, how experts and novices differ in their approach to problem solving, and why CPS is important in physics teaching and learning.

The beauty of physics lies in its parsimony—a small number of major principles can be used to solve a wide range of problems encompassing a wide range of contexts (Larkin, 1981b). Yet, beginning physics students do not perceive physics this way, but rather view physics as embodied in many equations—too many to be memorized. Although it is true that equations play a central role in physics both in terms of how they instantiate principles and concepts and how they are used in problem solving, to physicists equations are not viewed as things to be memorized. Experts can construct these equations easily on-the-fly by understanding the principles/concepts and the context in which they need to be applied. This ability is the hallmark of expertise in physics and takes years to achieve (Larkin, McDermott, Simon, & Simon, 1980).

Further, some aspects of physics problem solving at the introductory level often remain tacit, or at least are not made highly visible in the problem solving instruction provided by expert instructors (Reif & Heller, 1982). Next we describe what that tacit knowledge is, how experts use it to their advantage, and how novices circumvent using it.

Here we will deal with how experts solve problems in introductory physics, not how they solve novel problems that they have not seen before. It has been known for quite some time that experts focus on the major principles/concepts when asked for an approach needed to solve a problem or when asked to categorize problems according to similarity of solution (Chi et al., 1981, de Jong and Ferguson-Hessler, 1986, Hardiman et al., 1989). How do experts go about deciding which principles/concepts are fruitful to apply to a physics problem? The problem's context (e.g., story line, objects and how they are configured, variables/quantities given, quantity asked for) contains information that helps the expert decide if a particular principle/concept can be applied, and the specific form of the equation needed to instantiate the principle/concept to the particular problem context. The expert selects a likely principle/concept, justifies that it can be applied, and for the expert the principle is chunked with procedures /equations for applying it (Larkin, 1979). It is the justification process for applying the major principle/concept based on the question asked and the problem's story line that often remains tacit in traditional instruction (Larkin, 1981a).

For example, conservation of mechanical energy is a major idea in mechanics, and is relatively easy to apply in problem solving—one simply sets the mechanical energy (made up of the sum of potential and kinetic energies) in some initial state equal to the mechanical energy is some final state, and solves for whatever unknown is asked for in the particular problem under consideration. The hard part, and what often remains tacit, is how to decide if conservation of mechanical energy is a useful principle to select (Larkin, 1981a). To apply conservation of mechanical energy requires that no external nonconservative forces do work on the system under consideration. If some external force does work on the system, then the work-energy theorem (another major idea in mechanics related to conservation of mechanical energy) should be applied, not conservation of mechanical energy. Thus, an expert reading a problem that initially appears amenable to the application of conservation of energy checks the problem context to make sure that there are no external nonconservative forces doing work; if there aren’t, then conservation of mechanical energy is a good bet; if there are external nonconservative forces doing work, then the expert can switch seamlessly to considering the work-energy theorem as a viable alternative. To determine whether there are external nonconservative forces doing work, the expert looks for additional contextual cues such as whether there is friction, tension forces due to strings or ropes, or external agents pushing or pulling (Anzai and Yokoyama, 1984, de Jong and Ferguson-Hessler, 1991, Savelsbergh et al., 2002). Looking for these clues in the problem that lead to this decision is not trivial and can be very difficult for novices.

Finding such justifications for applying certain major ideas is something that experts do all the time, and something that most novices do not know how to do well, if at all. Examining justifications for applying a particular concept/principle in a problem context is a very useful expert skill and permeates mechanics, and indeed all of physics problem solving. In addition, successful problem solvers can generate an organized solution plan for how to apply principles (Finegold and Mass, 1985, Priest and Lindsay, 1992).

Novices can become rather proficient at physics problem solving, and eventually after much practice show the kind of top-down CPS approach described in the previous section (Eylon & Reif, 1984). But, this transition is difficult, especially since equation-based approaches such as means-ends analysis (Larkin, 1983, Larkin et al., 1980, Newell and Simon, 1972, Simon and Simon, 1978) tend to yield successful solutions a good portion of the time. The expert is able to use a top-down approach—the problem context and surface features trigger a possible principle/concept to select, then the context is checked to ensure there is an adequate justification for applying this principle/concept, and then the equation(s) needed to instantiate the principle/concept is generated (Larkin, 1983). The novice, however, uses the problem context to find equations that contain the variables in the problem. Then the novice tries to reduce the “distance” between the initial state and the goal state. For example, a problem asking for the velocity of a block after it has slid down a frictionless ramp might bring up equations with velocity, distance and time, with the solver looking to find enough equations for which the values are given or can be calculated in order to end up with the velocity of interest. Means-ends analysis is not as haphazard as it might sound—it often yields correct answers because novices can also rely on other clues to narrow their search for equations, such as which section of the book/course is currently being covered or what problems analogous to this one s/he has previously seen or solved.

We have broadly defined CPS above as a general approach for physics problem solving by which solvers integrate the selection of a principle/concept, its justification , and generate procedures for applying the principle/concept. The central thesis of the chapter is that teaching learners to use CPS provides both a deeper understanding of the domain and can even help in solving problems. In the sections that follow, we describe three studies that explore the value of different implementations of CPS with both college undergraduates and high school students. We begin with a study that compared a top-down approach for solving problems to a novice equation-centered approach. We then describe a teaching experiment conducted in a large undergraduate course that highlighted the role of principles, justifications, and procedures in problem solving. Finally, we describe an approach to implement CPS in high school physics instruction that allows for flexibility on the part of teachers. In all three cases, findings demonstrate that CPS is effective for helping students achieve a better understanding of how concepts relate to problem solving, as measured by a variety of assessments.

A Computer-Based Tool for Conceptual Problem Solving

Given students’ proclivity toward means-ends analysis as the preferred method for solving physics problems, one must think of ways of structuring and scaffolding students’ problem solving activities in order to elevate the usefulness of principles and concepts in problem solving. Experienced physics instructors will attest that simply telling students to use concepts more in their problem solving is typically met with blank stares. Our first attempt at CPS tried to structure the problem solving

A Classroom-Based Intervention of Conceptual Problem Solving at a University

Unlike the lab-based HAT studies described above, the study described in this section was conducted in the messy environment of a large (∼150 students) introductory calculus-based mechanics course for science and engineering majors. Given the propensity of novice physics students to rely on equation-based approaches for solving problems, an attempt was made to integrate an intervention in the course that not only illustrated a top down conceptual approach to solving problems but that also

A Classroom-Based Intervention of Conceptual Problem Solving at Several High Schools

The previous two implementations of CPS were done with high fidelity, the HAT studies in a controlled lab environment and the strategy writing implemented by a set of lecture and discussion instructors who agreed to follow a common regimen for an entire semester. In high schools there are numerous constraints that work against high fidelity implementation. Those include varying levels of teacher expertise in physics and teachers’ teaching styles, and time pressure to cover content and to

Concluding Remarks

We have reviewed different implementations of CPS with university and high school students both in carefully controlled laboratory studies as well as in the messy environment of real college and high school classrooms. The common feature across all of our implementations of CPS was the emphasis on conceptual analyses of problems, in particular attempting to illustrate how conceptual knowledge is used to solve problems and to make more explicit some of the tacit knowledge used by experts in


Work in part supported by the Institute of Education Sciences of the US Department of Education under Award No. DE R305B070085. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Institute of Education Sciences.

Knowledge of problem situations in physics: A comparison of good and poor novice problem solvers

Learning and instruction, categorization and representation of physics problems by experts and novices, cognitive science, internal models in physics problem solving, cognition & instruction, from problem solving to knowledge structure: an example from the domain of electromagnetism, american journal of physics, using multimedia learning modules to better prepare students for introductory physics lecture, physical review special topics – physics education research, cognitive structures of good and poor novice problem solvers in physics, journal of educational psychology.

Constraining novices to perform expertlike problem analyses: Effects on schema acquisition

The journal of the learning sciences, classtalk: a classroom communication system for active learning, journal of computing in higher education, effects of knowledge organization on task performance, cognition and instruction, differences in the process of solving physics problems between good problem solvers and poor problem solvers, research in science and technology education, interactive-engagement versus traditional methods: a six-thousand student survey of mechanics test data for introductory physics courses, the relation between problem categorization and problem solving among experts and novices, memory & cognition, force concept inventory, the physics teacher, students do not overcome conceptual difficulties after solving 1000 traditional problems, processing information for effective problem solving, engineering education, cognition of learning physics, enriching formal knowledge: a model for learning to solve textbook physics problems, new wine in old bottles: “repurposed” methodologies for studying expertise in physics.

The study of expertise has a long tradition in physics and has led to a deeper understanding of how content expertise is developed and used. After a brief overview of previous methodologies used to study expertise in physics, accompanying findings, and how those findings have enriched classroom practices, we describe three new approaches for exploring expertise in physics. The three approaches rely on methodologies common in cognitive science but are repurposed, or adapted, to study expertise in new ways within a science content area. We discuss findings from these studies, some surprising and some expected, then conclude with commentaries about the value of these approaches for studying expertise in science, and speculate on the implications of the findings for improving educational practices.

Developing an Educational Neuroscience of Category Learning

The disciplinary learning companion: the impact of disciplinary and topic-specific reflection on students’ metacognitive abilities and academic achievement, development of the sci-math sensemaking framework: categorizing sensemaking of mathematical equations in science, hybrid learning on problem-solving abiities in physics learning: a literature review, the impact of learning with the video conceptual understanding coach toward student conceptual understanding force concept, comparison of high temperature and high reynolds number water flows between ptb and nmij.

Bi-comparison for high temperature and high Reynolds number water flows was performed between Physikalische-Technische Bundesanstalt (PTB) and National Metrology Institute of Japan (NMIJ) using differential pressure meters. The flowrate range considered herein was from 250   m 3 /h to 740   m 3 /h and the temperature range was from 20   °C to 80   °C. The Reynolds number based on the pipe diameter ranges from 4.4×10 5 to 3.6×10 6 . The expanded uncertainty of volumetric flowrate with a coverage factor k =2 is 0.040% for PTB and 0.070% for NMIJ. The differential pressure meters as the transfer meter were a throat-tapped flow nozzle and an orifice. The results for both flow meters were in good agreement between the two labs. The difference in the average discharge coefficient at the same Reynolds number ranged from −0.05% to 0.03% for the flow nozzle and from −0.01% to 0.05% for the orifice. The results of the comparison indicate a high level of consistency between PTB and NMIJ for high temperature and high Reynolds number water flows.

Evaluation of the epidemiological and prognosis significance of ESR2 rs3020450 polymorphism in ovarian cancer

To investigate the correlation between the polymorphism of estrogen receptor β gene ( ESR2 ) rs3020450 and cancer susceptibility, and explore the epidemiological significance and the effect of ESR2 expression levels on the prognosis of ovarian cancer.

Based on meta-analysis the association between ESR2 rs3020450 polymorphism and cancer susceptibility was estimated and a case-control design was used to verify this result in ovarian cancer. The epidemiological effect of ESR2 rs3020450 polymorphism was assessed by attributable risk percentage ( ARP ) and population attributable risk percentage ( PARP ). Kaplan Meier plotters were used to evaluate overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients and GEPIA for the differential expression of ESR2 levels in ovarian cancer and adjacent normal tissues.

The pooled analysis indicated no significant correlation between the ESR2 rs3020450 polymorphism and the cancer susceptibility. In the stratified analysis by cancer types, significantly decreased risk was found in ovarian cancer (AG vs GG: OR = 0.73, 95%CI: 0.53–0.97, P  = 0.03). Unconditional logistic regression results of case-control study in ovarian cancer observed significant differences in all comparisons (AG vs GG: OR = 0.81, 95%CI: 0.62–0.98, P  = 0.04; AA vs GG: OR = 0.63, 95%CI: 0.42–0.92, P  = 0.01 and AG + AA vs GG: OR = 0.73, 95%CI: 0.53–0.96, P  < 0.001). Based on meta-analysis and case-control pooled results, ARP and PARP were evaluated respectively in allele (21.95% and7.97%), heterozygote (36.99% and 12.11%) and dominant model (36.84% and 12.97%) of rs3020450 polymorphism in ovarian cancer. The expression levels of ESR2 in normal tissues was significantly higher than that in cancer tissues (OV, Median, 4.7:0.21), and significant correlations were observed between high ESR2 expression levels and long OS (HR = 0.80, 95%CI: 0.70–0.92, P  = 0.002) and PFS (HR = 0.767, 95%Cl: 0.67–0.88, P  < 0.001).

Our results indicated that ESR2 rs3020450 polymorphism was associated with ovarian cancer risk from epidemiological perspective, and high ESR2 expression levels was associated with long survival in patients with ovarian cancer.

Research in Physics Education: A Study of Content Analysis

The current study is a content analysis of research in Physics education that was published between the years of 2008 and 2013 and was reached through the Turkish national academic network and information centre (ULAKBIM) and EBCSO. It is aimed to guide the researchers who are planning to conduct studies in this particular field by specifying tendencies of 105 articles on physics education in terms of their methods, subject areas, research titles, data analysis techniques, and sampling types. The data obtained from the study is presented with graphic, frequency and percentage tables. According to the research results, the physics education research was mostly carried out in 2013. When physics as subject titles is take into consideration, it is found that a large part of the studies are about mechanical physics and electric physics; however, when they are analyzed from the angle of their research titles, it seems that teaching methods and cognitive dimensions came into prominence. The findings of the study indicate that the majority of the physics education studies are quantitative in nature; achievement tests, interest tests, attitude tests and aptitude tests are mainly used as data collection instruments; a descriptive analysis method is used as data analysis method. Besides, the researchers preferred to choose their sample group from secondary school students and as a sample size they focused on groups of 31-100 students. The necessary recommendations are offered to the researchers of physics education in this study.

Philosophy in Physics Education

In this paper I will merely concentrate on the education of physics which I consider to be the most fundamental science. I investigate some fundamental problems, in physics education, determined by physicists and pedagogues. As far as I am concerned common problems in physics education are part of one global difficulty. And this difficulty stems from a weak inclusion of philosophy and particularly philosophy of science in science teaching. If science education research is concerned with what to teach and how to teach, my claims can be said to be only about the former. The latter, there is no doubt, is worth analyzing individually as well as the other problems concerning the introduction of historical and sociological aspects of science in science education. However, since it is not possible to detach the historical and sociological point of view from a philosophical concern, for the present purposes one should note that when I am speaking of philosophy, I am also referring to these aspects. In the first section I present a brief account of the fundamental problems met in physics education and later stress the importance of inclusion of philosophy in science education.

Myths about Learning

This section looks at some popular thoughts about learning, relating to “theories” surrounding learning and the paradigms (behaviorist, cognitivist and constructivist) that people use to justify their actions. The idea of adapting education to different styles of learning is investigated, along with learning pyramids and informal versus formal learning. We ask whether knowledge is really necessary in the Internet era, and how stable knowledge is. Could discovery learning allow us to learn more effectively? Is problem-based education the best way to learn to solve problems? Perceived gender differences in mathematics and the notion that school kills creativity are investigated, along with theories of multiple intelligences and non-verbal communication. Research on human memory shows that our memories are never wholly accurate. Effective learning methods include providing feedback, learning through concrete examples and taking breaks from learning.

Realization of a Software Application

It should be recalled that this book (Volume 3) follows the first two volumes. In order to have an independent book, we will remember the key concepts and constraints of the implementation of a software application.

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*** The 2020 Akron Regional Science Olympiad has been canceled. If you have any questions, please email [email protected] . ***

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Best Teaching Practices

Conceptual Understanding of Problem Solving

Research Findings

Research at the secondary and even post-secondary level on understanding of basic concepts that are involved in solving biology, chemistry, and physics problems (many of which require the application of algebraic or other mathematical concepts) indicates that students do not understand the concepts. This is confirmed by many research studies on problem solving in which students solve problems aloud. Research shows that even though students frequently solve mathematical problems correctly, they are unable to answer conceptual questions on which the problems are based.

Although there is a limited amount of research to indicate that understanding basic concepts qualitatively improves mathematical problem solving, it appears that this would be the case, especially for solving higher-level problems. Problem- solving research has led to the identification of commonly held scientific misconceptions, and to the conclusion that addressing these misconceptions in instruction may help to improve students' problem-solving ability.

In the Classroom

Many secondary students use algorithms to solve biology, chemistry, and physics problems that require the use of mathematics. They substitute data given in a problem into a formula (use the factor-label method, or a Punnett Square), perform appropriate mathematical operations, and arrive at a correct solution. However, when asked about the meaning of what they have done or requested to describe the variables and the relationship among the variables involved, they are unable to do so.

There is some evidence that having students perform numerous problems in this manner does not necessarily lead to conceptual understanding. If conceptual understanding is an expected outcome of science instruction, a more reasonable approach would be to first emphasize a qualitative understanding of the underlying concepts, including clarification of related student misconceptions. Then the use of mathematical problem solving should help provide students with deeper insight into the concepts.

For example, many students can calculate the density of a solid, yet when shown samples of identical mass but different volumes, are unable to serial order the samples by density. It is unlikely that having students solve numerous density problems by substituting values into the density formula will help them distinguish between density and volume.


Journal Articles - To access most of these Journal Articles, you must be a student, faculty or staff member at an OhioLINK affiliated institution. Access to OhioLINK may be available to Ohioans through their local, public, or school libraries. Contact OPLIN, INFOhio, or your local library for more information.

Individual Differences in Children's Addition and Subtraction Knowledge Cognitive Development , Vol. 19, Issue: 1, January - March, 2004. pp. 81-93 Canobi, Katherine H. Relations among patterns of conceptual and procedural knowledge and grade were examined in 90 six- to eight-year-olds in order to explore addition and subtraction development. Conceptual knowledge was assessed by examining children's responses to pairs of problems reflecting various part-whole relations. Children solved related problems as part of a Problem-solving Task, judged, and explained part-whole relations in a Judgement Task. Children also solved a random set of addition and...

Acquiring an Understanding of Design: Evidence from Children's Insight Problem Solving Cognition , Vol. 89, Issue: 2, September, 2003. pp. 133-155 Defeyter, Margaret Anne; German, Tim P. The human ability to make tools and use them to solve problems may not be zoologically unique, but it is certainly extraordinary. Yet little is known about the conceptual machinery that makes humans so competent at making and using tools. Do adults and children have concepts specialized for understanding human-made artifacts? If so, are these concepts deployed in attempts to solve novel problems? Here we present new data, derived from problem-solving experiments, which support the following...

The Cyclic Nature of Problem Solving: An Emergent Multidimensional Problem-Solving Framework Educational Studies in Mathematics , Vol. 58, Issue: 1, January 2005. pp. 45 - 75 Carlson, Marilyn P.; Bloom, Irene This paper describes the problem-solving behaviors of 12 mathematicians as they completed four mathematical tasks. The emergent problem-solving framework draws on the large body of research, as grounded by and modified in response to our close observations of these mathematicians. The resulting Multidimensional Problem-Solving Framework has four phases: orientation, planning, executing, and checking. Embedded in the framework are two cycles, each of which includes at least three of the four... This paper analyzes the results of a national examination from the perspective of conceptual learning versus algorithmic problem solving. A framework for thinking about knowledge and its organization is presented that can account for known expert-novice differences in knowledge storage and problem solving behavior. Interpreting any relationship between ability to answer qualitative and quantitative questions requires a model of cognition and that research should seek to develop assessments that monitor component aspects of developing expertise.

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What Are Conceptual Skills?

Definition and examples of conceptual skills.

Alison Doyle is one of the nation’s foremost career experts.

what is conceptual problem solving

Types of Conceptual Skills

Creative Thinking

Problem solving.

Luis Alvarez / Getty Images 

Conceptual skills help employees avoid the pitfall of not “seeing the forest for the trees,” as the saying goes. If you possess conceptual skills, you can both envision problems and brainstorm solutions. Having these skills likely means that you're a creative type, and can work through abstract concepts and ideas. 

Employers value conceptual skills, and in some roles, having them is essential. 

Find out more about the various varieties of conceptual skills, and why they're important. 

Conceptual skills allow someone to see how all the parts of an organization work together to achieve the organization’s goals. 

They're essential for leadership positions, particularly upper-management and middle-management jobs. Managers need to make sure everyone working for them is helping to achieve the company’s larger goals. Rather than just getting bogged down in the details of day-to-day operations, upper and middle managers also need to keep the company’s “big picture” aims in mind.

However, conceptual skills are useful in almost every position. 

Even when you have a particular list of duties, it is always helpful to know how your part fits into the broader goals of your organization. Plus, if you have conceptual skills, you can tackle big challenges that come up for your team and devise creative and thoughtful solutions that go beyond fulfilling rote tasks. 

Take a look at this list of the most important conceptual skills sought by most employers. It also includes sublists of related skills that employers tend to seek in job applicants.

Develop and emphasize these abilities in job applications, resumes, cover letters, and interviews. 

You can use these skills lists throughout your job search process. Insert the soft skills you’ve developed into your  resume  when you detail your work history, and highlight your conceptual abilities during interviews. 

A very important conceptual skill is the ability to  analyze  and evaluate whether a company is achieving its goals and sticking to its business plan. Managers have to look at how all the departments are working together, spot particular issues, and then decide what steps need to be taken.

Without strong  communication skills , an employee won’t be able to share their solutions with the right people. Someone with conceptual skills can explain a problem and offer solutions. They can speak effectively to people at all levels in the organization, from upper management to employees within a specific department. 

People with conceptual skills are also good  listeners . They have to listen to the needs of the employers before devising a plan of action.

People with conceptual skills must be very creative. They must be able to devise creative solutions to abstract problems, which involves thinking outside of the box. They must consider how all the departments within an organization work together, and how they can work to solve a particular problem.

Someone with conceptual skills also has strong leadership skills. They need to convince employees and employers to follow their vision for the company. They need to inspire others to trust and follow them, and that takes strong leadership.

Once an employee analyzes a situation and identifies a problem, they then have to decide how to solve that problem. People with conceptual skills are good at solving problems and making strong, swift decisions that will yield results.

Key Takeaways

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Conceptual skills definition and examples

Lyndsey McLaughlin

You start to apply for a job, and suddenly you find yourself being asked, what are your conceptual skills? You would be forgiven for being a bit stumped by this, as it’s not something we often see on job adverts. The irony is that mastering an understanding of conceptual skills could be seen as a conceptual skill in itself.

Gaining a deeper insight of what conceptual skills refer to will come in handy in every level of the job search journey. Not to mention that they will make you a more attentive and skilled worker when you are executing a role. Let’s take a look at the true meaning of conceptual skills and how we can use them to our advantage in the job search and in the workplace.

Here’s what we’ll explore in this blog:

What are conceptual skills?

How to improve conceptual skills

The definition of conceptual skills is the capacity to see the big picture in an organization. For example, instead of jumping headfirst to a project, you can understand why you are doing the project. How does it relate to the organization as a whole and how does it affects the external environment? In very basic terms, someone with conceptual skills can understand why something is being done. 

These skills are most relevant to management roles, as you are focused on achieving the wider goals of the organization. However, they can be useful in any other role too. It might not be obvious that you are being asked about your conceptual skills in an application or interview, it may be a question like this:

Can you tell us about a time when you were in a complex situation, and you had to identify possible issues to deliver a positive result?

An example of an answer to this question, which would portray your conceptual skills could be a situation where you are working as a manager in a local coffee shop, and a Starbucks opens on the same street. The potential issues with this would be competing with the pricing, quality, the popularity of the brand etc. These might affect the profitability of the business. 

A person with conceptual skills would be able to look at this situation and devise a plan to ensure there is minimum impact on the business. For instance, lowering prices, improving the brand, offering extras that a Starbucks wouldn’t be able to due to their branding, such as an instore bookstore, and perhaps even partnering up in some way with the brand. Without conceptual skills, you can only see the day-to-day and not the bigger picture.

What is the main difference between someone with conceptual skills, and someone without these skills?

Someone without conceptual skills will tend to just dive straight into a project or task, whereas someone with conceptual skills will take time to analyze all aspects that might affect the project or task. They see the big picture and the risks. 

Strengths in resumes

The best resumes are full of your strengths – not simply abilities you possess, but traits and knowledge that set you apart. Adding personal strengths in your resume allows you to highlight your expertise and mastery of the field. With the right resume strengths, hiring managers will quickly see you as a fierce contender for that perfect position.

Examples of conceptual skills

Part of the trick to understanding conceptual skills is being able to identify them in the first place. They can cover many skills that can be difficult to teach. Understanding what they are now will be useful for you to give a great response in an interview, as you will be more likely to pinpoint what the hiring manager is really asking you. Plus, you may spot language that could refer to some of these conceptual skills in the job ad. Understanding the intent behind these words will make you better prepared to include examples and experience that illustrate the relevant conceptual skills. 

Here are some conceptual skills examples that you may find useful in either of these scenarios and others:

The ability to analyze

Analysis skills are important conceptual skills in management. You need to be able to analyze various aspects of the business operations to ensure that every department is working towards the overall goals of the organization. The analysis would include being able to forecast, diagnose and understand any issues the business may face, and understand how to improve the business. Some skills that fall under analysis include research skills, data analysis, creativity, and critical thinking.

Strong communicator

Conceptual skills consist of the ability to communicate your solutions to others in an organization. For instance, if we use the practical example mentioned previously regarding the coffee shop competitor, the manager will not just find potential solutions but also feedback to other managers/colleagues. People with conceptual skills would need to be strong communicators, both verbal and listening. They would need to consider the needs of employees before devising a suitable plan of action. Some examples of strong communication skills would include, active listening, verbal and non-verbal, written, presentation, and ability to ask the right questions. 

Ability to solve problems

The ability to identify and solve problems is also an example of conceptual skills. Conceptual management skills require the ability to make quick decisions, where required. Some types of problem-solving skills include decision-making, critical thinking, logical thinking, multitasking, and troubleshooting.

What are the conceptual skills of a manager?

While employees at every level can benefit from improving their conceptual skills, managers are the most likely to face conceptual challenges on a day-to-day basis. Conceptual skills for managers include:

It is not enough for a manager just to identify and find solutions, they also need to be able to get others to follow their vision. Therefore, strong leadership skills are paramount, including the ability to develop a team, motivate and persuade others. Some typical skills associated with being a leader include management, delegation, team building, empathy, persuasion and flexibility.

The ability to ‘think outside the box’ and bring ideas to the table is important. In other words, to be creative. Some examples of creativity skills include strategic planning, open-mindedness, ability to formulate ideas, and collaboration.

While some people may be naturally inclined to look at the bigger picture and evaluate problems before starting a task, all of us can improve our conceptual skills with practice. Here are some steps you can take:

How to improve your conceptual skills

As you can see, conceptual skills often refer to things that can be difficult to study. That's not to say that you can't acquire or improve conceptual skills that you are lacking. In fact, a big part of your professional journey will involve using some of the following tips to keep on top of yours. As with anything, a lot of these skills will best be perfected through practice and experience.

That's why practice in managing projects can go a long way in developing your conceptual skills. Managing a project of any size can give you a broader perspective of an entire work process. That's something you can bring to multiple roles and industries in the future. For the same reason, advocating for yourself in order to gain experience in a leadership can help you to gain experience and new ways of thinking.

However, if opportunities to take on more responsibility are thin in your job, all hope is not lost. There are plenty of other actions that you can work into your day to day that will improve your conceptual skills. You can start by being intentional about the way that you communicate with your colleagues. Consider how you can make yourself better understood and better understand the people you interact with in your job for smoother work processes. Think about your other regular tasks and where you can be more intentional. Taking the time to analyze and problem solve things that have become second nature could go a long way to developing better conceptual skills while you're at it.

Conceptual skills when applying for jobs

It might not be obvious when you are being asked about your conceptual skills. However, if you are asked about your ability to identify and find a solution to a problem, the answer is related to your conceptual skills. Both your resume and your cover letter can be excellent spots to state some of the conceptual skills you possess. Conceptual skills examples may include:

Try to think of anecdotes or examples that effectively demonstrate times when you have exhibited those conceptual skills. In your resume, this could look like choosing to include a bullet point about a relevant project. On your cover letter, on the other hand, you'll have a little more freedom to dive into the details of a time when you have shown relevant conceptual skills. Explaining how you demonstrated the conceptual skill is a stronger way of illustrating your value than simply claiming that you possess that particular skill. Just remember to keep the conceptual skills you illustrate relevant to the job that you are applying to!

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Resume, résumé or resumé: Is this word spelled with accents or not?


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