problem solving higher biology

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

1.3: Problem Solving

• Last updated
• Save as PDF
• Page ID 8377

Problem Solving

Educators and employers alike have all argued strongly in recent years that the ability to solve problems is one of the most important skills that should be taught to and nurtured in university students. Medical, professional, and graduate schools alike look for students with demonstrated ability to solve problems; the MCAT has even recently changed its format to more specifically assess student’s ability to solve problems. Life is full of problems to solve, irrespective of the profession one chooses. Effective problem-solving skills are important!

Despite a clear demand for this skill set, it is surprisingly rare to find problem solving taught explicitly in formal educational settings, particularly in core science courses where the transmission and memorization of “facts” usually take precedence.

In BIS2A, we want to start changing this. After all, nobody really cares if you’ve memorized the name or catalytic rate of the third enzyme in the citric acid cycle (not even standardized tests), but a lot of people care if you can use information about that enzyme and the context it functions in to help develop a new drug, design a metabolic pathway for making a new fuel, or help understand its importance in the evolution of biological energy transformations.

Your instructors believe that the ability to solve problems is a skill like any other. It is NOT an innate (i.e. you’ve either got it or you don’t) aptitude. Problem solving can be broken down into a set of skills that can be taught and practiced to mastery. So, even if you do not consider yourself a good problem solver today, there is no reason why you can’t become a better problem solver with some guidance and practice. If you think that you are already a good problem solver, you can still get better.

Cognitive scientists have thought about problem solving a lot. Some of this thinking has focused on trying to classify problems into different types. While problems come in many different flavors (and we’ll see some different types throughout the course), most problems can be classified along a continuum of how well-structured they are.

At one end of the continuum are well-structured problems . These are the types of problems that you usually encounter in school. They usually have most of the information required to solve the problem, ask you to apply some known rules or formulas, and have a pre-prescribed answer. On the other end of the continuum are ill-structured problems . These are the types of problems you will usually face in real life or at work. Ill-structured problems are often poorly defined and usually do not include all of the information required to solve them. There may be multiple ways of solving them, and even multiple possible “correct” outcomes/answers.

Note: Possible Discussion

Well-structured problems (like the story problems you might often encounter in text books) are often set in an artificial context, while the ill-structured problems one faces in day-to-day life are often set in a very specific context (your life). Is it possible for multiple people to observe the same situation and perceive different problems associated with it? How does context and perception influence how one might identify a problem, its solution, or its importance? To have a fruitful/enriching discussion it pays to start by presenting an example AND some direct reasoning. Replies that acknowledge the initial comment and either provide an extension of the original argument (by way of a new perspective or example) or provide a reasoned counter-argument the are most valuable follow-ups.

Problems can also be “simple” or “complex,” depending on how many different variables need to be considered to find a solution. They can also be considered as “dynamic” if they change over time. Other problem classification schemes include story problems, rule-based problems, decision-making problems, troubleshooting problems, policy problems, design problems, and dilemmas. As you can see, problem solving is a complicated topic, and a proper, in-depth discussion about it could take up multiple courses. While the topic of problem solving is fascinating, in BIS2A we aren’t interested in teaching the theories of problem solving per se. However, we ARE interested in teaching students skills that are applicable to solving most types of problems, giving students an opportunity to practice these skills, and assessing whether or not they are improving their problem-solving abilities.

Note: Since we are asking you to think explicitly about problem solving, it is fair to expect that your ability to do so will be evaluated on exams. Do not be surprised by this. We are going to incorporate problem solving into the class in a number of different ways:

• We will be explicitly teaching elements of problem solving in class.
• We will have some questions on the study guides that encourage problem solving.
• We will make frequent use of the pedagogical tool we call the “Design Challenge” to help structure our discussion of the topics we cover in class.

When we are using the Design Challenge in class, we are working on problem solving. Within the context of the Design Challenge, your instructor may also present other specific concepts related to problem solving – like decision-making. Slides will be marked explicitly to engage you to think about problem solving. Your instructor will also remind you verbally on a regular basis.

Browse Course Material

Course info, instructors.

• Prof. Hazel Sive
• Prof. Tyler Jacks
• Dr. Diviya Sinha

Departments

As taught in.

• Biochemistry
• Cell Biology
• Developmental Biology
• Molecular Biology

Learning Resource Types

Introductory biology, teaching students to solve problems.

In this section, Prof. Hazel Sive describes this course’s focus on problem solving.

Problem Solving at MIT

I think the unofficial motto of MIT is “We solve problems.” Everything that we do here is to prepare our students to be problem solvers in the world. This idea permeates all the disciplines at MIT: engineering; science; business; architecture and urban planning; and humanities, arts, and social sciences. No matter what degree students earn at MIT, they leave with the ability to solve hard problems. When faced with a new problem, they know how to understand it, think about ways to solve it, try those ways, and ultimately get some kind of solution. That kind of philosophical and also real power gives students a big edge when they leave MIT and enter the workforce, go to graduate school, or go to medical school and become a physician.

It’s a difficult way to learn, but it’s a fantastic way to learn. I believe that learning should be a struggle; without struggle, you don’t get anywhere new. I think the courses at MIT are very challenging, and the introductory courses here are much harder than the introductory courses at most other universities.

Learning Terminology and Facts in Order to Solve Problems

Our course does include some rote learning, but the purpose of this rote learning is for our students to develop enough background to be able to speak the subject and understand and tackle challenging problems. They have to know what DNA is, what a gene is, and what a cell is. Very often, I’ll give them a term and I’ll say, “This is the scientific term. You should know it because it’s in your book, it’s in the scientific literature, and you’ll hear it on the news. But what’s most important is the concept underlying the term.” If you look at our problem sets and exams, you’ll see that there are no questions where students have to label a diagram, give a definition, or regurgitate facts.

Learning to Problem Solve through Practice

"It’s a terrific moment when a student realizes that this is different from any way they’ve been taught before, and they’re going to be challenged in ways in which they never knew they could be challenged."

In this course, students learn to solve problems through practice. Every two weeks, we give the students a problem set with six long problems. The problems are all about problem solving. The students look at the problems and realize that this isn’t just a matter of taking the lecture material and giving it back to us; we assume they know that information, and they’re expected to build from there. It’s very challenging for the students.

This is a shock to many of our students. In most high schools and even universities, biology is about learning facts. This was the case for me. I went to a very good university in South Africa. I learned all about the anatomy of the skull. I learned all about bones. I could classify fish. I learned many things that are very useful, but no one ever taught me how to solve a problem. Many of our students arrive at MIT having gotten the highest possible mark on the Advanced Placement ® biology exam, and when they get the first problem set in our course, they are stunned. They haven’t encountered biology as a kind of detective story where there’s a problem that they need to understand and solve. We explain that biology is a rigorous problem solving discipline; in fact, biology is all about using information to solve problems. It’s a terrific moment when a student realizes that this is different from any way they’ve been taught before, and they’re going to be challenged in ways in which they never knew they could be challenged.

The first problem set has to do with biochemistry. By the time they get the problem set, we’ve taught them about the various classes of molecules and macro-molecules that are found in living cells. We give them a problem set where not only do they have to be able to recognize something about the macro-molecules we present to them, they also have to recognize something about how the macro-molecules are put together, about bonding between the different parts of the macro-molecules, and about what that means for the structure of the macro-molecule, especially proteins. We do that both on paper and then also using a visualization program that was developed in the biology department called StarBiochem . In this program, the students are given a 3-dimensional structure of a protein, and they have to be able to understand what they’re looking at and what it means for the actual function of the protein, which is usually an enzyme that can catalyze a particular reaction. As soon as they see that problem set, they realize that this is going to be different from their high school biology experience.

As another example, when students learn about medical disorders, we don’t ask them to regurgitate the typical symptoms. Instead, we might say, “Here’s a patient that’s presenting with a funny disorder, and if she tries to move too quickly, she collapses. Her muscles look normal. Her nerves look normal, but if you do certain tests to them, you can see they’re not firing properly. Here’s what the trace of their firing pattern looks like. Suggest what’s wrong with the patient.”

The Problem Set Process

I tell the students that they have to practice these problems on their own. We can give pointers about how to solve the problems, but they need to think through the material. I tell them that when I’m thinking hard, I get a headache. For them, it might come as some other manifestation, but they should be getting their own personal version of a headache when they’re doing their problem sets. It shouldn’t be easy, but once they learn how to do a problem and get somewhere with a problem, it’s powerful. It empowers them to then go and tackle another one.

"Through this process, students show themselves that they can triumph over the work, and they come out actually having some power over the material."

For each problem set, I tell the students to print out three copies. First, students should take one copy and attempt the problem set all by themselves, without their notes and without help from others. They can identify what they don’t understand right away. They might get halfway through the problem set and panic upon realizing that they don’t know very much, that they went to lecture but didn’t absorb a lot of the material.

At that point, they can review their notes and their textbook, or go to the library, or search for information on the web. They learn what they can, then try the second problem set copy. Again, this is without help from other people; they need to personally struggle with the material. They always get farther the second time. The headache, the struggle, and then the triumph with bits of the problems is really powerful. Through this process, students show themselves that they can triumph over the work, and they come out actually having some power over the material.

Usually there will still be some holes in what they’re able to do and understand. Then, they can go and talk to their friends, their teaching assistants, and me or my co-instructor. They have to hand in their own work, in their own words, but they can work together and their work can have all these inputs. If they work as a group initially, they may miss getting that headache because they’re relying on their friends and people who may get it more quickly or in a different way than they do. I really discourage them from working together initially because I think they just don’t learn the material properly that way. They need to learn by doing. As they do more and more problems, they get better at addressing these questions. Students get substantial practice throughout the semester, and they come out really knowing something about how to solve problems in this particular area of life science.

Crafting Good Problems for 7.013

A critical part of our job as teachers is crafting good problems. We aim to create problems that have the following characteristics:

• Rooted in problem solving . A good problem should challenge students to think and to apply their knowledge in novel ways.
• Clearly written and easily understandable . The point of the problem should be clear.
• Built upon multiple aspects of the course material . Although the course is taught in a modular way, students cannot forget the earlier material as they learn new material. The early, fundamental material is used for all of the later lectures and problem sets. The best problems not only address the current module that they’re learning, but also draw upon and integrate past modules. For example, while learning about neurobiology, students should still remember that proteins only function properly if they’re put in the correct place in a cell.
• Informed by current literature . When possible, we like to draw upon real, current examples from the news and/or scientific literature. We usually take just one aspect of it and use it in a problem. When possible, we try to pick topics that we think students can relate to. This way, our problems are fresh, current, and interesting, and we never run out of ideas for problems.

You are leaving MIT OpenCourseWare

• STEM Ambassadors
• School trusts
• ITE and governors
• Invest in schools
• STEM careers inspiration
• Benefits and impact
• Our supporters
• Become a STEM Ambassador
• Request a STEM Ambassador
• Employer information
• Training and support
• STEM Ambassadors Partners
• Working with community groups
• Search icon
• Join the STEM Community

Questions in Higher biology

Designed for use as a companion to Higher Biology Course Notes, this is an invaluable revision tool for all students in the approach to exams. A multiple choice section and key short answer questions will enable students to improve data handling and problem solving skills to raise exam achievement.

A summary of essential knowledge and understanding points linked to the Higher Biology Arrangement is provided at the beginning of each set of topic questions. In addition, a comprehensive Biology vocabulary list and pull-out answer section aids revision and ultimately helps students to enhance their performance and gain exam success.

Show health and safety information

Please be aware that resources have been published on the website in the form that they were originally supplied. This means that procedures reflect general practice and standards applicable at the time resources were produced and cannot be assumed to be acceptable today. Website users are fully responsible for ensuring that any activity, including practical work, which they carry out is in accordance with current regulations related to health and safety and that an appropriate risk assessment has been carried out.

This is a physical resource. Come and visit the National STEM Learning Centre library to see it.

Find out more about the Centre

Share this resource

Did you like this resource.

Problem-Solving in Biology Teaching: Students’ Activities and Their Achievement

• Published: 21 July 2023
• Volume 22 , pages 765–785, ( 2024 )

Cite this article

• Nataša Nikolić   ORCID: orcid.org/0000-0001-8460-0430 1 &
• Radovan Antonijević   ORCID: orcid.org/0000-0003-4959-376X 1  

286 Accesses

1 Altmetric

Explore all metrics

Problem-solving is, by nature, a creative process which, by teaching through the implementation of research and discovery activities, allows students to create their knowledge, revise it and link it to broader systems. The aim of the research was to describe and analyse the process of solving biological problems through activities that are performed during the process of solving them, as well as to study how the implementation of these activities affects the level and quality of student achievement in biology. This study employed a quantitative method research strategy to describe the problem-solving process in biology teaching and determine student achievement. Data collection was by means of survey and testing. A Likert-scale survey and a biology knowledge test were constructed for the purposes of the research. For data analysis, descriptive statistics, factor analysis and the Pearson correlation coefficient were used. The data of eighth-grade students were collected from September 2016 to February 2017, in 72 schools in Serbia (565 students). The factor analysis confirmed that problem-solving activities could be grouped into the following five areas: (1) analysing and planning problem-solving; (2) discovering solution(s) to the problem; (3) problem-solving evaluation activities; (4) additional activities involving the discussion of the problem; (5) the degree of student independence in the process of discovering a solution to a problem. The results show that with the increasing frequency of the realisation of the research problem-solving activities, the achievement of students also increases. With regard to achievement quality, a positive but low correlation was found in all three domains—knowledge acquisition, understanding and application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Studying the student’s perceptions of engagement and problem-solving skills for academic achievement in chemistry at the higher secondary level

Sankar E. & A. Edward William Benjamin

The Impact of Problem-Based Learning on Students’ Achievement in Mechanical Waves in Secondary Schools

Stella Teddy Kanyesigye, Jean Uwamahoro & Imelda Kemeza

Research mathematicians’ practices in selecting mathematical problems

Morten Misfeldt & Mikkel Willum Johansen

Ali, A. R., Toriman, M. E., & Gasim, M. B. (2014). Academic achievement in biology with suggested solutions in selected secondary schools in Kano State, Nigeria. International Journal of Education and Research, 2 (11), 215–224.

Google Scholar  

Anderson, J. R. (2005). Cognitive Psychology and Its Implications: Sixth Edition . Macmillan.

Aryulina, D., & Riyanto, R. (2016). A problem-based learning model in biology education courses to develop inquiry teaching competency of preservice teachers. Cakrawala Pendidikan, 35 (1), 47–57. https://doi.org/10.21831/cp.v1i1.8364

Article   Google Scholar  

Antonijević, R., & Nikolić, N. (2019). The role of problem-oriented teaching in the process of the development of critical and creative thinking. In V. Orlović Lovren, J. Peeters & N. Matović (Eds.), Quality of education: Global development goals and local strategies (pp. 49–63). Institute for Pedagogy and Andragogy.

Bowden, E. M. (1997). The effect of reportable and unreportable hints on anagram solution and the Aha! experience. Consciousness & Cognition, 6 (4), 545–573. https://doi.org/10.1006/ccog.1997.0325

Article   CAS   Google Scholar  

Bruner, J., Goodnow, J., & Austin, A. (1956). A study of thinking . Wiley.

Cai, J., & Brook, M. (2006). Looking back in problem-solving. Mathematics Teaching, 196 , 42–45.

Chin, C., & Chia, L. G. (2006). Problem-based learning: Using ill-structured problems in biology project work. Science Education, 90 (1), 44–67. https://doi.org/10.1002/sce.20097

Article   ADS   Google Scholar  

Chin, C., & Osborne, J. (2008). Students’ questions: A potential resource for teaching and learning science. Studies in Science Education, 44 (1), 1–39. https://doi.org/10.1080/03057260701828101

Costa, V., & Sarmento, R. P (2019). Confirmatory factor analysis–A case study . Retrieved from https://arxiv.org/ftp/arxiv/papers/1905/1905.05598.pdf

DeVellis, R. F. (1991). Scale development: Theory and applications . Sage Publications Inc.

Dunlap, J. C. (2005). Problem-based learning and self-efficacy: How a capstone course prepares students for a profession. Educational Technology Research and Development, 53 (1), 65–85. https://doi.org/10.1007/BF02504858

Etherington, M. B. (2011). Investigative primary science: A problem-based learning approach. Australian Journal of Teacher Education, 36 (9), 53–74. https://doi.org/10.3316/ielapa.328484780726539

Gagné, E. D., Yekovich, C. W., & Yekovich, F. R. (1993). The cognitive psychology of school learning (2nd ed.). HarperCollins College Publishers.

Gbore, L. O., & Daramola, C. A. (2013). Relative contributions of selected teachers’ variables and students’ attitudes toward academic achievement in biology among senior secondary schools students in Ondo State. Nigeria. Current Issues in Education, 16 (1), 1–11.

Gijbels, D., Dochy, F., Van den Bossche, P., & Segers, M. (2005). Effects of problem-based learning: A meta-analysis from the angle of assessment. Review of Educational Research, 75 (1), 27–61. https://doi.org/10.3102/00346543075001027

Harun, N. G., Yusof, K. H., Jamaludin, M. Z., Helmi, S. A., & Hassan, S. (2012). Motivation in problem-based learning implementation. Procedia-Social and Behavioral Sciences, 56 (2012), 233–242.

Hoskinson, A. M., Caballero, M. D., & Knight, J. K. (2013). How can we improve problem-solving in undergraduate biology? Applying lessons from 30 years of physics education research. CBE Life Sciences Education, 12 (2), 153–161.

Article   PubMed   PubMed Central   Google Scholar  

Hurst, R. W., & Milkent, M. M. (1996). Facilitating successful prediction problem-solving in biology through application of skill theory. Journal of Research in Science Teaching, 33 (5), 541–552.

Jamari, D., Mohamed, H., Abdullah, Z., Mohd Zaid, N., & Aris, B. (2018). Biology problem-solving: The high achiever students. European Proceedings of Social and Behavioural Sciences, 68 , 831–842.

Kapa, E. (2001). A metacognitive support during the process of problem solving in a computerized environment. Educational Studies in Mathematics, 47 (3), 317–336.

Kirui, J. M., & Kaluyu, V. (2018). Influence of selected psychosocial factors on learners’ performance in science subjects: A case of public secondary schools in Moyale Sub-County, Kenya. International Journal of Education and Research, 6 (1), 15–28.

Kolber, B. J. (2011). Extended problem-based learning improves scientific communication in senior biology students. Journal of College Science Teaching, 41 (1), 32–39.

Meiring, S. P. (1980). Problem solving. A basic mathematics goal, parts 1 and 2 . GLC Publishers, Agincourt.

Montague, M. (2005). Math problem solving for upper elementary students with disabilities (p. 8). The Access Center: Improving Outcomes for All Students K.

Nehm, R. H. (2010). Understanding undergraduates’ problem-solving processes. Journal of Microbiology & Biology Education, 11 (2), 119–122. https://doi.org/10.1128/jmbe.v11i2.203

Okoye, N. S., & Okechukwu, R. N. (2006). The effect of concept mapping and problem-solving teaching strategies on achievement in genetics among Nigerian Secondary School Students. African Journal of Educational Studies in Mathematics and Sciences, 4 , 93–98.

Polya, G. (1973). How to solve it: A new aspect of mathematical method  (2nd ed.). Princeton University Press.

Ranjanie, B. (2017). Impact of problem-based learning on teaching biology for higher secondary students. International Journal of Current Research, 9 (12), 62932–62934.

Sabella, M. S., & Redish, E. F. (2007). Knowledge organization and activation in physics problem solving. American Journal of Physics, 75 (11), 1017–1029.

Squires, J. E., Estabrooks, C. A., Newburn-Cook, C. V., & Gierl, M. (2011). Validation of the conceptual research utilization scale: An application of the standards for educational and psychological testing in healthcare. BMC Health Services Research, 11 (1), 1–14.

Stanisavljević, J. D., & Đurić, D. Z. (2012). Efekat primene problemske nastave biologije na trajnost i kvalitet stečenih znanja [The effect of the application of problem-based biology teaching on the durability and quality of acquired knowledge]. Uzdanica, 9 (1), 303–312.

Sungur, S., Tekkaya, C., & Geban, O. (2006). Improving achievement through problem-based learning. Journal of Biological Education, 40 (4), 155–160.

Syafii, W., & Yasin, M. R. (2013). Problem-solving skills and learning achievements through problem-based module in teaching and learning biology in high school. Asian Social Science, 9 (12), 220–228. https://doi.org/10.5539/ass.v9n12p220

Thakur, P., & Dutt, S. (2017). Problem-based learning in biology: Its effect on achievement motivation of students of 9th standard. International Journal of Multidisciplinary Education and Research, 2 (2), 99–104.

Trauth Nare, A., Buck, G., & Beeman-Cadwallader, N. (2016). Promoting student agency in scientific inquiry: A self-study of relational pedagogical practices in science teacher education. In G. Buck & V. Akerson (Eds). Allowing our professional knowledge of pre-service science teacher education to be enhanced by self-study research: Turning a critical eye on our practice (pp. 43–67). Springer.

Woolfolk, A., Hughes, M., and Walkup, V. (2013). Psychology in Education (2nd ed.). Pearson Education.

Xun, G., & Land, S. M. (2004). A conceptual framework for scaffolding III-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52 (2), 5–22. https://doi.org/10.1007/BF02504836

Download references

Author information

Authors and affiliations.

Department of Pedagogy and Andragogy, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia

Nataša Nikolić & Radovan Antonijević

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nataša Nikolić .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Nikolić, N., Antonijević, R. Problem-Solving in Biology Teaching: Students’ Activities and Their Achievement. Int J of Sci and Math Educ 22 , 765–785 (2024). https://doi.org/10.1007/s10763-023-10407-5

Download citation

Received : 29 June 2021

Accepted : 07 July 2023

Published : 21 July 2023

Issue Date : April 2024

DOI : https://doi.org/10.1007/s10763-023-10407-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Biology teaching
• Problem-solving
• Problem-solving activities
• Students’ achievement
• Find a journal
• Publish with us
• Track your research

• Higher Biology Key Facts- All Units [doc 223KB]
• HIigher Biology Words [doc 26KB]
• Higher Terminology [doc 26KB]
• Higher Biology Exam Advice [doc 24KB]
• Past Paper Questions by Unit [docx 24KB]
• Data handling and Experimental Design [docx 18KB]
• Unit 1- MC X 70 [pdf 2MB]
• Unit 1- MC X 70- Answers [docx 15KB]
• Unit 2- MC X 70 [pdf 617KB]
• Unit 2- MC X 70- Answers [docx 14KB]
• Unit 3- MC X 70- Answers [docx 14KB]
• Unit 3- MC X 70 [pdf 2MB]
• H Biology all 2013 [pdf 2MB]
• mi H Biology all 2013 [pdf 233KB]

• Unit 1 Homework Booklet- Part 1 [docx 8MB]
• Unit 1 Homework Booklet- Part 2 [docx 10MB]
• Unit 2 Homework Booklet [pdf 5MB]
• Unit 3 Homework Booklet [docx 2MB]

• Publishing Policies
• For Organizers/Editors
• For Authors
• For Peer Reviewers

Biology Problem-Solving: The High Achiever Students

Problem-solving has been acknowledged as one of the compulsory skills needed to face and overcome challenges of the modern world in either learning or everyday life. However, there is limited information regarding the level of problem-solving skills demonstrated by school students in learning biology subject compared to physics and mathematics. This study aims to identify the problem-solving level of 16-year old high achievers in selected boarding school in the Southern and Central Regions of Malaysia. The problem-solving skills of 70 students were measured using a validated open-ended test, UKPM, which consists of general and topic-specific problem-solving questions for biology. These questions focus on the different steps in the problem-solving processes. High achievers from boarding schools were chosen to ensure the homogenous background of the participants. The data were descriptively analysed and the overall score was used to determine the students’ problem-solving level based on the classification in Programme of International Students Assessment (PISA). The result showed that the majority of the participants are low (35%) and intermediate (64%) problem solvers and they showed incompetence in manipulating information and making justifications. They possess high tendency to find the absolute answer, but lack the reflecting ability when answering the test. The criteria and limitations showed that the participants are prone to practise a converged thinking pattern. In this, educators should introduce innovative alternative teaching and learning approach need to enhance the students’ problem-solving skills. Keywords: Problem-solving Skills Problem-solving Processes Biology School

Introduction

Problem-solving (PS) skills refer to a person’s ability to make critical judgment and decision based on the appropriate justification of the problem’s situation and its surrounding ( Kivunja, 2014 ). Solving a problem requires an individual to explore the root cause of a problem and create potential solutions pragmatically by using logic, lateral, and creative thinking ( Ismail & Atan, 2011 ). This approach is parallel with the 21st-century learning that emphasised on the construction of new knowledge, a shift from focusing solely on rote memorisation and classroom knowledge transfer in schools that have become habitual over the years. Problem-solving (PS) PS is not an innate skill ( Bal & Esen, 2016 ) therefore, providing the students with the chance to solve the problem is actually an effective way to develop this skill ( Shute, Ventura, & Ke, 2015 ; Shute & Wang, 2013 ). Learning instruction that emphasises on the understanding of core concept helps in developing students’ PS as this skill is best learnt through the use of domain-specific problem-solving activities that are challenging for students to learn ( Prevost & Lemons, 2016 ). During the process, increasing students’ understanding of the topic will help them to create and relate to the new knowledge. In the aspect of learning and education, the repetition cycle of the PS process through practices will equip them with PS skills that can be applied in different problems regardless of the context, discipline, or situation ( Yang, 2012 ). In this light, PS skills can have long-term benefits and subsequently, help the students to take charge of their profession, personal encounter and everyday hurdles ( Bal & Esen, 2016 ; Syafii & Yasin, 2013 ). Moreover, biologists agreed that students should acquire PS skills in order to learn biology better ( Hoskinson, Caballero, & Knight, 2013 ).

The numerous proposed models on PS stipulate that the basic component of the PS process is to identify problems, to suggest solutions, to apply solutions, and to reflect at the end of the process. One may interpret that the problem-solving process is the sequential steps in a linear process. However, in reality, most individuals demonstrate flexible and inventive approaches based on the different circumstance and they do not adhere to an perpetual linear PS process ( Yu, Fan, & Lin, 2014 ). PS skills are taught through the integration with the teaching of different subjects, fields, domains, or contents, the steps, process, or stages remained unchanged, therefore, understanding the meanings and function of each PS step is crucial for the success of problem-solving. PS step can be used as a guideline on what to observe and measure in evaluating the proficiency of PS. Besides that, teachers will know the types of learning support or scaffolding that have to be given to the students during the teaching and learning process.

PS requires a variety of mental skills, including interpreting information, planning, trying alternative strategies, reflecting and decision-making. However, studies have found that students are not aware of the processes taking place in problem solving ( Yu et al., 2014 ). Early PS studies demonstrated that students have difficulties in PS steps, especially when tackling the orientation stage, which is to identify the problem. In this regard, the first step of PS is vital and students should be taught for a better understanding of this component. Hence, this component should be set as the rubric or benchmark during PS measurement and assessment. Studies done in both general and domain specific PS affirmed that the students’ incapability in solving a problem does not stemmed from their lack of knowledge or skills specific domain, rather, it is due to the failure of properly identifying the source of the problem and its details. This can be seen in PS studies that compared the differences between novice and expert problem solvers where PS performance is highly influenced by individual ability to understand the problem, as well as analysing the potential answer or solution ( Prevost & Lemons, 2016 ). Nevertheless, enhancing students’ PS skills is one of the prior goals of all educational institutions therefore, developing PS skills is necessary in order to improve students’ ability in scientific thinking especially in science subjects, such as biology ( Ulusoy, Turan, Tanriverdi, & Kolayis, 2012 ; Yenice, Ozden, & Evren, 2012 ). In other words, PS skills should be developed early as in the students’ schooling years.

Previous studies have shown that Malaysia students face difficulty in problem-solving ( Abd Razak, Mohd Johar, Andriani, & Yong, 2014 ; Johnny, Abdullah, Abu, Mokhtar, & Atan, 2017 ; Kaus, Phang, Ali, Abu Samah, & Ismail, 2017 ). On the other hand, the term ‘problem-solving’ is commonly synonymous with obvious calculation and this resulted in the lack of study on problem-solving associated with Biology. In this regard, despite the differences in problems’ structure and contents between science subjects, the instructional purpose, which is to elucidate the patterns and processes in the natural world and systems, align comparatively with each other. Research noted that solving biology problem requires the engagement of the same skill practised by physicists and biologists ( Hoskinson et al., 2013 ). Nevertheless, compared to mathematics or physics there is still a prominent gap in the research of PS skills in biology for the past three decades( Kim, Prevost, & Lemons, 2015 ).

Problem Statement

The implementation of PS in pedagogical activities has led to the measurement of PS skills among the students. Studies have shown that there is a significant positive relationship between academic achievement, career success, and certain habits of mind or behaviour with skills competencies ( OECD, 2014 ; Stecher & Hamilton, 2014 ; Wüstenberg, Greiff, & Funke, 2012 ). In this light, it is more challenging to measure the competency level of PS skills compared interpersonal skills, therefore, there are guidelines that can be used in developing the instrument or selecting the rubrics to measure PS skills. On the other hand, these higher-order PS skills are arduous to be measured and the discrepancies on relevant and credible measurement scales are still debatable among the researchers ( McCoy, Braun-Monegan, Bettesworth, & Tindal, 2015 ; Stecher & Hamilton, 2014 ). By observing and measuring these PS processes, this study will obtain valuable information related to the cognitive habit in ones’ mind when solving a task. Observing and measuring these processes during intervention study will provide formative information as well as evidence of the student’s development of PS skills. There is a lack of information about problem-solving skills for biology and how students solve biology problems, among school students is still in its formative stages.

Research Questions

The research questions that lead this study are as follows:

What is the students’ problem-solving competency level for Biology?

How is the students’ performance in non-routine Biology questions in terms of the problem-solving steps in problem-solving process?

Purpose of the Study

For the purposes of this article, domain-specific problem-solving refers to topic Cell Division of secondary school biology syllabus investigate the PS level of the students. Therefore, this study aims to identify the students’ abilities regarding the steps in PS as well as their problem-solving competency level for Biology.

Research Methods

This study was participated by 70 science stream students (39 females and 31 males) who are 16 years old from three high-achieving fully residential schools located in the Central and Southern Regions of Malaysia. The students were chosen to ensure the homogenous background of the participants. Their PS skills for the biology subject were measured using the UKPM, which is a validated open-ended test with 20 topic-specific questions in Section A and 20 general questions in Section B. The topic-specific PS questions are related to cell division, while the general PS questions are related to biology or science as well as questions adapted from the problem-solving domain in Programme for International Student Assessment (PISA). This study referred to the Ge & Land PS Model that comprises four problem-solving steps, which are identifying problems (PS1), giving suggestions and options to solve the problem (PS2), making justification (PS3), and reflecting the action (PS4) ( Bixler, 2007 ). A total of 10 questions were allocated for each PS step and each question focuses on the different steps in the PS process. The maximum score for the UKPM is 120 and the data were analysed descriptively to identify the participants’ performance for each step in the PS process. The assessment rubric was adapted from previous research ( Bixler, 2007 ) while the PS classification of the competency level was done by referring to the OECD or ‘Organisation for Economic Cooperation and Development’ classification that was used in PISA ( OECD, 2014 ). The UKPM was validated prior to the research by experts in the PS domain, as well as against the biology syllabus for the Malaysian secondary school.

Table 2 summarises the participants’ scores. The results show that neither the female nor male participants score excellently in the UKPM test. The mean score for female participants was 42.92, and the difference is not distinct with the male participants with the mean score of 42.42. The overall achievement did not reach 50% of the overall score with only 42.70. The dispersion of the score patterns for all groups is almost similar, with the average of 8. The highest score is 62/120, while the lowest is 26/120. Both extreme scores were obtained by female participants.

Each individual score was compared and classified according to the six PS competency level as presented by Organisation for Economic Co-operation and Development (2014). Diagram 1 shows the percentage of the number of participants in each competency level; only 1% of the participants could be classified as possessing level 4 competencies. The majority of the participants (64%) could be categorised as level 3 problem solvers, while 35% could be classified as having level 2 competencies.

The level of problem-skill for each gender was compared against and there are only minor differences. Diagram 2 shows that 2% of the female participants possess level 4 competence. Moreover, there is a 4% difference between the number of females (36%) and males (32%) who demonstrated level 2 competence. The same difference was observed in level 3 and there is only a 6% difference between both groups.

Table 3 describes the score of each PS step in details. Out of the four steps in the PS process, making reflection (PS4) has the lowest mean score of 9.33 ± 3.90 for females and 9.39 ± 3.35 for males. The mean scores show that most participants are capable to obtain at least 10 out of the total 40 marks allocated for PS4 in UKPM. Although the other three steps have higher mean scores, they are still considered to be in the low range as none of the PS steps are able to reach at least 50% of the mean score compared to the allocated marks. The minimum and maximum scores for each PS step are in the lower range as the highest score is 21 (PS4) and the lowest score is 3 (PS2). Diagram 3 summarises the findings related to the PS steps. In this light, there are no major differences in the overall achievement each PS steps between each gender.

The results provide the insights on students’ behaviour when solving problems during the biology subject. The biology subject is different from physics and mathematics; this is because, the calculations only play minimal roles compared to reading and understanding the fact. It was discovered that the participants from both genders have poor knowledge and capabilities in all the PS steps, and consequently, they obtained poor results in the PS domains based on the Programme International Student Assessment (PISA).

It was discovered that the participants did not plan well and did not evaluate the situation in the questions. In one of the PS1 questions, the participants were asked to list all the barriers and factors that they should consider before choosing the most appropriate option and only a small percentage of students managed to list the appropriate answers beyond the question given, while the rest only listed down a few factors that could be found in the question. In another PS1 question, the participants were required to propose an arrangement plan regarding the number of people to be placed in eight rooms. For this question, the participants should consider the criteria given when proposing the arrangement. The researcher expected the participants to perform some calculations, however, the majority of them presented wrong answers even though a draft table was provided to assist them in planning and evaluating the problem by providing specific directions for the key stages. They only provided answers that they are familiar despite the expectation that they would be able to find the solution when they delve deeper into the question. This shows excellent achievement in public school examination will not ensure good competency in non-routine PS as most school examination revolves around routine problem (Abd Razak et al., 2014)

In the meantime, the planning process is seldom practised in answering open-ended problems even though it is commonly used in routine and algorithmic problems. Hence, it should be considered in developing the skills to solve open-ended problems ( Reid & Yang, 2002 ). In this light, most past PS studies only focused on the earlier PS steps, which are identifying the root cause of the problem and planning the solutions and actual success in PS is actually determined based on the capability to determine what is needed to be solved and how to do it effectively ( Ulusoy et al., 2012 ).

Identifying the root cause of the problem and planning the solutions are categorised as knowledge acquisition according to the PS framework in PISA ( OECD, 2014 ) or rule identification and application ( Schweizer, Wüstenberg, & Greiff, 2013 ; Wüstenberg et al., 2012 ). It is assumed that these particular steps are more utilised in higher-order thinking skills (HOTS) compared to the later steps in PS model. Meanwhile, reflections and monitoring require judgement and deep thinking and reflections can be done in most of the PS steps. Due to the huge influence in PS stages, some studies have divided the ‘Identify Problem’ and ‘Solution Planning’ steps into smaller sub-steps (E.g., ‘Gather Info’). Some studies also added other indicators that are relative to these steps (E.g., ‘Avoiding Problem’ and ‘Flexibility’) with a specific checklist of criteria that has to be observed in the study. These additions were based on the researchers’ perspectives and research needs.

The results revealed that the students are confused and facing difficulties in linking the function of spindle fibre with mitosis or meiosis failure (Figure 4 ). The participants’ lack of understanding of the key concepts has contributed to the poor results for the PS steps. As an example, the question related to the concepts of meiosis and its functions in producing haploid gamete cell which are different in terms of numbers of chromosomes, genetic content due to random desegregation, and crossing over processes. In this light, the students were unable to answer the question even though it is just slightly different from the examination format questions (Figure 5 ). This shows that the students were confused and the students had come out with varied segments of inaccurate response and totally incorrect answers. On the other hand, the success rate was improved when the same question was modified with additional explicit hints or organised to be similar to the pattern of the examination format questions. Without this explicit linkage, the participants had difficulties in linking what they had learnt on the idea of cell division with examination questions. It seems that solving previous exam questions and drill practices are common in a biology lesson so that the students could ace their examination. In this light, despite their impressive results, the students tend to have limitations in terms of their level of thinking skills. These students tend to answer the questions through memorisation, and the drilling practices create a mind model and schema that will be stored in their minds, rather than creating understanding of the principles. In other words, the students memorise the content and they face difficulties when presented with questions in a new context or structure.

Step four in the PS process is making reflection. In UKPM, questions in PS4 prompt the participants to present their agreement on the topic (Figure 6 ). For example, the students are provided with a formula as a guide for their answers and to answer PS4 questions, students are expected to review and identify the formula and they have to suggest the correct formula when giving their justifications. Unfortunately, there were not many participants who were able to present a sound reflection. Most of them only provide their answers by referring to the given calculation without reflecting and they also provided incorrect answers. As a result, they scored very low for PS4 which affected their overall score. This shows that the learners have low abilities and face difficulties in creating a link between skills and knowledge ( Reid & Yang, 2002 ). Moreover, this study also observed the habit and pattern related to how the participants answer the test. Besides that, at the end of the UKPM test, the researcher had obtained verbal feedbacks from students who seemed to not prefer lengthy questions as they only glanced through the instruction and provide answers without any description, explanation, or justification. It was found that these students are more familiar with routine questions, which only require one right answer and they looked uncomfortable when asked to answer non-routine abstract questions that require giving opinions and justifications and consequently, gave opinions that did not reflect the lesson that they had learnt.

In the meantime, a good problem solver has three characteristics, which are having a good conceptual understanding of the domain involved, including domain-specific skills and being able to adjust wisely to the use of automated skills. This is because PS requires two types of knowledge namely declarative and procedural knowledge that are interdependent during the PS activity ( Yu et al., 2014 ). Expert problem solvers are more mature when performing an integrated mental representation of the problem, as well as demonstrating a better understanding of the core concepts, nature, and form of the problem ( Prevost & Lemons, 2016 ). They need more time to define and understand the issues compared to novice problem solvers who prefer to complete the task impatiently and often ignore the PS steps, including the first and most important step, which is problem identification ( Yu et al., 2014 ). The same pattern could also be observed among primary, secondary, and college students. Novice problem solvers usually seek the solution without the definite understanding of the problem and they lacked the ability to reflect their own performances. They tend to overlook the analysis and reflection process, even though they knew that they were stuck with an inappropriate solution during the process.

The flexible, linear, and sequential PS processes can be practised differently according to the problem solvers’ creativity and needs, as well as the situation. However, the students’ lack of understanding on these PS processes will affect their perceptions on the processes’ progression and nature. Furthermore, research ( Yu et al., 2014 ) reported a similar pattern in the early phase of their study. They found that students lacked the flexibility and creativity as they opted for the linear mode of an incomplete PS process. Therefore, it is important to incorporate effective teaching strategies to enhance the students' understanding on the meaning and function of each PS step, and the students can develop individual skills when solving problems. A good problem solver that practises effective monitoring step would consistently reflect the chosen strategy to ensure that they are on the right track, as well as checking for other solutions. The students should be able to monitor and steer the direction of their own progress, to ask questions among themselves that could help to maximise the effective strategies and to prevent themselves from constantly using the unproductive approach in generating solutions (Jamari, Mohamed, Abdullah, Mohd Zaid, & Aris, 2017b). Choosing an effective strategy without making revisions or having self-correcting mechanisms to monitor the progress of PS is comparable to those who fail to choose good method and strategy right from the start. This issue can contribute to the failure in PS. Therefore, students must be encouraged to make verification by monitoring and reflecting their choice that could increase their HOTS and PS level.

Previous studies suggested that instructional scaffolding is necessary in aiding the students’ problem-solving processes (Jamari, Mohd Zaid, Abdullah, Mohamed, & Aris, 2017a; Kim et al., 2015 ). In this light, it is important to focus on content specific scaffolding, which is also known as conceptual scaffolding in school. This is because mastering the content of the lesson is the ultimate goal of having learning assistance, either with or without instructional materials or the presence of a teacher. Therefore, the teacher is responsible to help students in understanding the function of each step involved in the PS in class regardless of subject or domain ( Yenice et al., 2012 ). The action of mentioning these processes during the teaching and learning process without giving the students with the opportunity to perform activities that require them to think, learn and practise each step will not enhance the students’ PS skills ( Yu et al., 2014 ). The similarities between ill-structured task and common everyday problems make it worthwhile to inculcate and develop the students’ PS skills. PS skills helps to cater the needs of solving multiple tasks in a short term which refers to schooling and learning and at the same time shaping an individual to be a capable problem solver later in life as a long term goal. Since an ill-structured task usually has complex structure and may have numerous potential solutions, this type of task requires more cognitive activity to process all the problems’ information in the attempt to find the best solution.

Promising instructional strategies to enhance HOTS and PS have been widely studied and including the inquiry learning approach and focused on STEM education (Jamari et al., 2017b). However, there are still not much studies being done on these approaches for the Malaysian context although there are plenty of studies done in other countries. Examples of teaching strategies that emphasised on authentic ill-structured problem that can be applied by teachers include case-based learning (CBL) and problem-based learning (PBL). The problem or task does not stem from the textbook, but from the everyday problems which require the application of the similar concepts or principles. CBL and PBL are categorised under inquiry and they are suitable to be used in the environment of science learning due to their potential to attract the interest of students, to spark inquiry, and to encourage them to continue exploring the task ( Herreid, Schiller, & Herreid, 2012 ; Pai et al., 2010 ). Teacher’s face to face or online involvement provides the suitable guidance to help them interpret and accelerate active information transfer processes by providing a learning environment that can develop HOTS and PS skills ( Kivunja, 2014 ; McCoy et al., 2015 ).

Although time is a factor that can affect the development of PS in an individual, it is important to expose the students to PS steps and processes so that they can learn and practise these skills to become a competent citizen. Therefore, teaching approaches and strategies that emphasise on authentic problem and active learning such as Inquiry Learning and STEM (Science, Technology, Engineering and Mathematics) education should be combined with appropriate instructional scaffolding that focuses on the students’ ability to master the lesson, as well as nurturing and developing their PS skills ( Bybee, 2010 ; Moore, Johnson, Peters-Burton, & Guzey, 2016 ; Tseng, Chang, Lai, & Chen, 2013 ). In the meantime, this study has several limitations. One of the limitations is the small number of participants as the study was focused on high achievers. Hence, the sample for this study may not represent the whole population. Nevertheless, the sample provides insights on how high achieving students conduct PS. This information will add to the body of knowledge on problem solving in the context of Malaysian school students. It is assumed that other students are facing the same PS problems as shown by the high achievers, therefore, future research can be implemented on students from different categories and backgrounds. A PS research with more focus on the biology subject can be conducted by replicating this research to other topics in the biology syllabus..

• Abd Razak, N. N. F., Mohd Johar, A., Andriani, D., & Yong, C. Y. (2014). Mathematical Problem Solving Ability Among Form Two Students. Jurnal Pendidikan Matematik, 2(2), 1-13.
• Belgin Bal, İ., & Esen, E. (2016). Problem Solving Skills of Secondary School Students. China-USA Business Review, 15(6). doi:10.17265/1537-1514/2016.06.002
• Bixler, B. A. (2007). The Effects of Scaffolding Students' Problem-Solving Pocess Via Question Prompts on Problem Solving and Intrinsic Motivation In An Online Learning Environment. (Doctor of Philosophy phd thesis), Pennsylvania State University,
• Bybee, R. W. (2010). Advancing STEM Education A 2020 Vision. Technology And Engineering Teacher, 70(1), 30-35.
• Herreid, C. F., Schiller, N. A., & Herreid, K. F. (2012). Science Stories: Using Case Studies to Teach Critical Thinking. VA, USA: National Science Teachers Association.
• Hoskinson, A. M., Caballero, M. D., & Knight, J. K. (2013). How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research. CBE Life Sci Educ, 12(2), 153-161. doi:10.1187/cbe.12-09-0149
• Ismail, S., & Atan, A. (2011). Aplikasi Pendekatan Penyelesaian Masalah Dalam pengajaran Mata Pelajaran Teknikal dan Vokasional di Fakulti Pendidikan UTM. Journal of Educational Psychology and Counseling, 2, 113-144.
• Jamari, D., Mohamed, H., Abdullah, Z., Mohd Zaid, N., & Aris, B. (2017b). Fostering Higher Order Thinking And Problem Solving Skills Through Social Media. Man In India, 97(12), 1-10.
• Jamari, D., Mohd Zaid, N., Abdullah, Z., Mohamed, H., & Aris, B. (2017a). Instructional Scaffolding To Support Ill-Structured Problem Solving A Review. Sains Humanika, 9(1-4), 33-39.
• Johnny, J., Abdullah, A. H., Abu, M. S., Mokhtar, M., & Atan, N. A. (2017). Difficulties In Reasoning Among High Achievers When Doing Problem Solving In Mathematics. Man In India, 97(12), 61-70.
• Kaus, M. A., Phang, F. A., Ali, M. B., Abu Samah, N., & Ismail, A. K. (2017). Problem Solving And Social Supports: The Roles of Parents. Man In India, 97(12), 279-287.
• Kim, H. S., Prevost, L., & Lemons, P. P. (2015). Students' usability evaluation of a Web-based tutorial program for college biology problem solving. Journal of Computer Assisted Learning, 31(4), 362-377. doi:10.1111/jcal.12102
• Kivunja, C. (2014). Do You Want Your Students to Be Job-Ready with 21st Century Skills? Change Pedagogies: A Pedagogical Paradigm Shift from Vygotskyian Social Constructivism to Critical Thinking, Problem Solving and Siemens’ Digital Connectivism. International Journal of Higher Education, 3(3). doi:10.5430/ijhe.v3n3p81
• McCoy, J. D., Braun-Monegan, J., Bettesworth, L., & Tindal, G. (2015). Do Scaffolded Supports between Aspects of Problem Solving Enhance Assessment Usability? Journal of Education and Practice, 6(36), 175-185.
• Moore, T., Johnson, C. C., Peters-Burton, E. E., & Guzey, S. S. (2016). The need for a STEM road map: A framework for integrated STEM education. In (pp. 33 -12). NY: Routledge Taylor & Francis Froup.
• OECD. (2014). PISA 2012 Results: Creative Problem Solving Students’ skills in tackling real-life problems Volume V. Retrieved from La rue André-Pascal, PARIS
• Pai, A., Benning, T., Woods, N., McGinnis, G., Chu, J., Netherton, J., & Bauerle, C. (2010). The Effectiveness of a Case Study-Based First-Year Biology Class at a Black Women's College. Journal of College Science Teaching, 40(2), 32.
• Prevost, L. B., & Lemons, P. P. (2016). Step by Step: Biology Undergraduates' Problem-Solving Procedures during Multiple-Choice Assessment. CBE Life Sci Educ, 15(4). doi:10.1187/cbe.15-12-0255
• Reid, N., & Yang, M.-J. (2002). Open-ended problem solving in school chemistry: A preliminary investigation. International Journal of Science Education, 24(12), 1313-1332. doi:10.1080/09500690210163189
• Schweizer, F., Wüstenberg, S., & Greiff, S. (2013). Validity of the MicroDYN approach: Complex problem solving predicts school grades beyond working memory capacity. Learning and Individual Differences, 24, 42-52. doi:10.1016/j.lindif.2012.12.011
• Shute, V. J., Ventura, M., & Ke, F. (2015). The power of play: The effects of Portal 2 and Lumosity on cognitive and noncognitive skills. Computers & Education, 80, 58-67. doi:10.1016/j.compedu.2014.08.013
• Shute, V. J., & Wang, L. (2013). Measuring Peoblem Solving Skills In Portal 2. In IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2013) (pp. 33-39). Fort Worth, Texas, USA: International Assn for Development of the Information Society (IADIS).
• Stecher, B. M., & Hamilton, L. S. (2014). Measuring Hard-to-Measure Student Competencies A Research and Development Plan (13 978-0-8330-8806-2). Retrieved from Santa Monica, California:
• Syafii, W., & Yasin, R. M. (2013). Problem Solving Skills and Learning Achievements through Problem-Based Module in teaching and learning Biology in High School. Asian Social Science, 9(12). doi:10.5539/ass.v9n12p220
• Tseng, K. H., Chang, C. H., Lai, S. J., & Chen, W. P. (2013). Attitudes towards science, technology, engineering and mathematics (STEM) in a project-based learning (PjBL) environment. International Journal of Technology Design Education, 23, 87-102.
• Ulusoy, Y. O., Turan, H., Tanriverdi, B., & Kolayis, H. (2012). Comparison of Perceived Problem Solving Skills of Trainee Students Graduated from Different. Procedia - Social and Behavioral Sciences, 46, 2099-2103. doi:10.1016/j.sbspro.2012.05.435
• Wüstenberg, S., Greiff, S., & Funke, J. (2012). Complex problem solving — More than reasoning? Intelligence, 40(1), 1-14. doi:10.1016/j.intell.2011.11.003
• Yang, Y. T. C. (2012). Building virtual cities, inspiring intelligent citizens: Digital games for developing students’ problem solving and learning motivation. Computers & Education, 59(2), 365-377. doi:10.1016/j.compedu.2012.01.012
• Yenice, N., Ozden, B., & Evren, B. (2012). Examining of Problem Solving Skills According to Different Variables for Science Teachers Candidates. Procedia - Social and Behavioral Sciences, 46, 3880-3884. doi:10.1016/j.sbspro.2012.06.165
• Yu, K.-C., Fan, S.-C., & Lin, K.-Y. (2014). Enhancing Students’ Problem-Solving Skills through Context-Based Learning. International Journal of Science and Mathematics Education, 13(6), 1377-1401. doi:10.1007/s10763-014-9567-4

Copyright information

About this article

Publication date.

01 May 2018

Article Doi

https://doi.org/10.15405/epsbs.2018.05.68

978-1-80296-039-6

Future Academy

Print ISBN (optional)

Edition number.

1st Edition

Business, innovation, sustainability, environment, green business, environmental issues

Cite this article as:

Jamari, D., Mohamed, H., Abdullah, Z., Zaid, N. M., & Aris, B. (2018). Biology Problem-Solving: The High Achiever Students. In M. Imran Qureshi (Ed.), Technology & Society: A Multidisciplinary Pathway for Sustainable Development, vol 40. European Proceedings of Social and Behavioural Sciences (pp. 831-842). Future Academy. https://doi.org/10.15405/epsbs.2018.05.68

We care about your privacy

We use cookies or similar technologies to access personal data, including page visits and your IP address. We use this information about you, your devices and your online interactions with us to provide, analyse and improve our services. This may include personalising content or advertising for you. You can find out more in our privacy policy and cookie policy and manage the choices available to you at any time by going to ‘Privacy settings’ at the bottom of any page.

Manage My Preferences

You have control over your personal data. For more detailed information about your personal data, please see our Privacy Policy and Cookie Policy .

These cookies are essential in order to enable you to move around the site and use its features, such as accessing secure areas of the site. Without these cookies, services you have asked for cannot be provided.

Third-party advertising and social media cookies are used to (1) deliver advertisements more relevant to you and your interests; (2) limit the number of times you see an advertisement; (3) help measure the effectiveness of the advertising campaign; and (4) understand people’s behavior after they view an advertisement. They remember that you have visited a site and quite often they will be linked to site functionality provided by the other organization. This may impact the content and messages you see on other websites you visit.

Higher Biology & Human Biology Resources

Materials for the study of H SQA Biology & Human Biology.

SQA Past Papers - Higher

Sqa past papers - revised higher, sqa past papers - old higher.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• CBE Life Sci Educ
• v.15(4); Winter 2016

Step by Step: Biology Undergraduates’ Problem-Solving Procedures during Multiple-Choice Assessment

Luanna b. prevost.

† Department of Integrative Biology, University of South Florida, Tampa, FL 33620

Paula P. Lemons

‡ Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602

Associated Data

Findings from a mixed-methods investigation of undergraduate biology problem solving are reported. Students used a variety of problem-solving procedures that are domain general and domain specific. This study provides a model for research on alternative problem types and can be applied immediately in the biology classroom.

This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this allowed us to systematically investigate their problem-solving procedures. We identified a range of procedures and organized them as domain general, domain specific, or hybrid. We also identified domain-general and domain-specific errors made by students during problem solving. We found that students use domain-general and hybrid procedures more frequently when solving lower-order problems than higher-order problems, while they use domain-specific procedures more frequently when solving higher-order problems. Additionally, the more domain-specific procedures students used, the higher the likelihood that they would answer the problem correctly, up to five procedures. However, if students used just one domain-general procedure, they were as likely to answer the problem correctly as if they had used two to five domain-general procedures. Our findings provide a categorization scheme and framework for additional research on biology problem solving and suggest several important implications for researchers and instructors.

INTRODUCTION

The call to reform undergraduate education involves shifting the emphasis in science classes away from rote memorization of facts toward learning core concepts and scientific practices ( National Research Council [NRC], 2003 ; American Association for the Advancement of Science [AAAS], 2011 ). To develop instruction that focuses on core concepts and scientific practices, we need more knowledge about the concepts that are challenging for students to learn. For example, biology education research has established that students struggle with the concepts of carbon cycling (e.g., Anderson et al. , 1990 ; Hartley et al ., 2011 ) and natural selection (e.g., Nehm and Reilly, 2007 ), but we know much less about students’ conceptual difficulties in ecology and physiology. Researchers and practitioners also need to discover how students develop the ability to use scientific practices. Although these efforts are underway (e.g., Anderson et al. , 2012 ; Gormally et al. , 2012 ; Dirks et al. , 2013 ; Brownell et al. , 2014 ), many research questions remain. As research accumulates, educators can create curricula and assessments that improve student learning for all. We investigate one key scientific practice that is understudied in biology education, problem solving ( AAAS, 2011 ; Singer et al. , 2012 ).

For the purposes of this article, we define problem solving as a decision-making process wherein a person is presented with a task, and the path to solving the task is uncertain. We define a problem as a task that presents a challenge that cannot be solved automatically ( Martinez, 1998 ). Problem-solving research began in the 1940s and 1950s and focused on problem-solving approaches that could be used to solve any problem regardless of the discipline ( Duncker and Lees, 1945 ; Polya, 1957 ; Newell and Simon, 1972 ; Jonassen, 2000 , 2012 ; Bassok and Novick, 2012 ). Despite the broad applicability of these domain-general problem-solving approaches, subsequent research has shown that the strongest problem-solving approaches derive from deep knowledge of a domain ( Newell and Simon, 1972 ; Chi et al. , 1981 ; Pressley et al. , 1987 ). Domain is a term that refers to a body of knowledge that can be broad, like biology, or narrow, like ecosystem structure and function. This body of literature has developed into a theoretical framework called domain-specific problem solving. We situate our research within this theoretical framework.

THE THEORETICAL FRAMEWORK OF DOMAIN-SPECIFIC PROBLEM SOLVING

Domain-specific problem solving has its origins in information-processing theory (IPT; Newell and Simon, 1972 ). IPT focuses on the cognitive processes used to reach a problem solution and emphasizes the general thinking processes people use when they attempt problem solving, such as brainstorming ( Runco and Chand, 1995 ; Halpern, 1997 ) and working backward by beginning with the problem goal and working in reverse toward the initial problem state ( Newell et al. , 1958 ; Chi and Glaser, 1985 ). Despite the empirical evidence for general thinking processes, one of IPT’s shortcomings as a comprehensive view of human cognition ( Dawson, 1998 ) is that the knowledge base of the problem solver is not considered.

Domain-specific problem solving expands IPT to recognize that experts in a particular domain have a relatively complete and well-organized knowledge base that enables them to solve the complex problems they face (e.g., Chase and Simon, 1973 ). One of the landmark studies showing the differences between the knowledge base of experts and nonexperts, or novices, was conducted in science, specifically in physics. Chi and colleagues (1981) compared the classification of physics problems by advanced physics PhD students (i.e., experts) and undergraduates who had just completed a semester of mechanics (i.e., novices), identifying fundamental differences. Chemistry researchers built on Chi’s work to identify differences in how experts and novices track their problem solving and use problem categorization and multiple representations ( Bunce et al ., 1991 ; Kohl and Finkelstein, 2008 ; Catrette and Bodner, 2010 ). Biology researchers built upon this work by conducting similar problem-solving studies among experts and novices in evolution and genetics ( Smith, 1992 ; Smith et al. , 2013 ; Nehm and Ridgway, 2011 ). Taken together, these studies established that experts tend to classify problems based on deep, conceptual features, while novices classify problems based on superficial features that are irrelevant to the solution.

Domain-specific problem-solving research within biology also has revealed important individual differences within groups of problem solvers. These studies show that wide variation in problem-solving performance exists. For example, some novices who solve problems about evolution classify problems and generate solutions that are expert-like, while others do not ( Nehm and Ridgway, 2011 ). This research points to the importance of studying variations in problem solving within novice populations.

Given the centrality of the knowledge base for domain-specific problem solving, it is necessary to describe the components of that knowledge base. Domain-specific problem-solving research recognizes three types of knowledge that contribute to expertise. Declarative knowledge consists of the facts and concepts about the domain. Procedural knowledge represents the how-to knowledge that is required to carry out domain-specific tasks. Conditional knowledge describes the understanding of when and where to use one’s declarative and procedural knowledge ( Alexander and Judy, 1988 ). Note that the field of metacognition also uses this three-type structure to describe metacognitive knowledge, or what you know about your own thinking ( Brown, 1978 ; Jacobs and Paris, 1987 ; Schraw and Moshman, 1995 ). However, for this paper, we use these terms to describe knowledge of biology, not metacognitive knowledge. More specifically, we focus on procedural knowledge.

Procedural knowledge consists of procedures. Procedures are tasks that are carried out automatically or intentionally during problem solving ( Alexander and Judy, 1988 ). Procedures exist on a continuum. They can be highly specific to the domain, such as analyzing the evolutionary relationships represented by a phylogenetic tree, or general and applicable to problems across many domains, such as paraphrasing a problem-solving prompt ( Pressley et al. , 1987 , 1989 ; Alexander and Judy, 1988 ).

APPLYING DOMAIN-SPECIFIC PROBLEM SOLVING TO MULTIPLE-CHOICE ASSESSMENT IN BIOLOGY

We used domain-specific problem solving to investigate the most common form of assessment in the college biology classroom, multiple-choice assessment ( Zheng et al ., 2008 ; Momsen et al ., 2013 ). College biology and science, technology, engineering, and mathematics (STEM) courses rely on multiple-choice assessment due to large enrollments, limited teaching assistant support, and ease of scoring. Outside the classroom, multiple-choice assessment is used on high-stakes exams that determine acceptance to professional schools, like the Medical College Admissions Test and Graduate Record Exam. To our knowledge, the framework of domain-specific problem solving has not been applied previously to investigate multiple-choice assessment in college biology.

It has become common practice within the biology education community to think about assessment, including multiple-choice assessment, by determining the Bloom’s taxonomy ranking of assessment items (e.g., Bissell and Lemons, 2006 ; Crowe et al. , 2008 ; Momsen et al. , 2010 , 2013 ). Bloom’s Taxonomy of Educational Objectives was built to facilitate the exchange of test items among faculty; it was not based primarily on the evaluation of student work ( Bloom, 1956 ; Anderson and Krathwohl, 2001 ). Bloom’s taxonomy helps educators think about the range of cognitive processes they could ask their students to perform and has served as an invaluable resource enabling educators to improve alignment between learning objectives, assessments, and classroom curricula (e.g., Crowe et al. , 2008 ). When applying Bloom’s taxonomy to assessment items, items are ranked as remembering, understanding, applying, analyzing, evaluating, and synthesizing. Items ranked as remembering and understanding are grouped as lower-order items; and items ranked as applying, analyzing, evaluating, and synthesizing are grouped as higher-order items ( Zoller, 1993 ; Crowe et al. , 2008 ). Despite the value of Bloom’s taxonomy for instructors, what is not known is the relationship between the procedural knowledge of domain-specific problem solving and the Bloom’s ranking of biology assessments. This is a critical gap in the literature, because efforts to improve student learning in college science classrooms may be stymied if critical insights about student work from domain-specific problem solving are not linked to our understanding of assessment and curricular design.

In the study reported here, we used the theoretical lens of domain-specific problem solving to describe the procedural knowledge of nonmajors in an introductory biology course. We addressed the following research questions:

• What are the domain-general and domain-specific procedures students use to solve multiple-choice biology problems?
• To what extent do students use domain-general and domain-specific procedures when solving lower-order versus higher-order problems?
• To what extent does the use of domain-general or domain-specific procedures influence the probability of answering problems correctly?

Setting and Participants

We recruited participants from a nonmajors introductory biology course at a southeastern public research university in the Spring 2011 semester. One of the authors (P.P.L.) was the course instructor. The course covered four major areas in biology: evolution, ecology, physiology, and organismal diversity. The instructor delivered course content using lecture interspersed with clicker questions and additional opportunities for students to write and discuss. Students also completed five in-class case studies during the semester; students completed cases in self-selected small groups and turned in one completed case study per group for grading. In addition to group case studies, the instructor assessed student learning via individual exams. Students also received points toward their final grades based on clicker participation.

In the second week of the semester, the instructor announced this research study in class and via the course-management system, inviting all students to participate. Students who volunteered to participate by completing an informed consent form were asked to produce written think-alouds for problems on course exams throughout the semester. One hundred sixty-four students completed an informed consent form. Of the 164 consenting students, 140 students actually produced a written think-aloud for at least one of 13 problems; of the 140 students, 18 did written think-alouds for all 13 problems. The remainder of students did written think-alouds for one to 13 problems. On average, research participants provided written think-alouds for 7.76 problems.

The 164 consenting students represented 73.9% of the course enrollment ( n = 222). The 164 consenting students included 70.8% females and 29.2% males; 20.4% freshmen, 40.9% sophomores, 24.1% juniors, and 13.9% seniors. The 164 students were majoring in the following areas: 3.7% business, 1.5% education, 4.4% humanities, 11.0% life and physical sciences, 5.9% engineering, and 72.3% social sciences.

This research was conducted under exempt status at the University of Georgia (UGA; IRB project 201110340).

Data Collection

Problem development..

We wrote 16 multiple-choice problems to include in this study. All problems related to material dealt with during class and focused specifically on ecosystems, evolution, and structure–function relationships. On data analysis, three problems were excluded, because most students were confused by the wording or visual representations or were able to solve the problem correctly with a superficial strategy. Each problem was preceded by a prompt for students to provide their written think-aloud (see Written Think-Alouds section). Each problem was also labeled with a preliminary Bloom’s taxonomy categorization ( Anderson and Krathwohl, 2001 ). A summary of all problems, including a description, the preliminary Bloom’s ranking, and the faculty consensus Bloom’s ranking, is provided in Table 1 . As an example, one of the final 13 problems is shown in Figure 1 . All other problems are shown in Supplemental Figure S1.

Sample problem from the domain of evolution used to probe students’ problem-solving procedures. The preliminary ranking that students saw for this question was Applying and Analyzing based on Bloom’s taxonomy. Experts ranked this problem as Analyzing. The correct answer is E. Images of benthic and limnetic males are courtesy of Elizabeth Carefoot, Simon Fraser University.

Summary of problems used for data collection

For each problem, a description is included along with the preliminary Bloom’s ranking, and the final consensus Bloom’s ranking. The actual problems are included in Supplemental Figure S1.

Ranking of Problems by Bloom’s Level.

We wanted to investigate the use of domain-general or domain-specific procedures in lower-order versus higher-order problems. We asked three biology faculty members who were not investigators in this study to rank the Bloom’s levels of the problems we developed. The biology faculty members were selected because they have extensive teaching experience in college biology and also have experience ranking assessment items using Bloom’s taxonomy. The faculty used a protocol similar to one described previously ( Momsen et al. , 2010 ). To assist with Bloom’s ranking, we provided them with class materials relevant to the problems, including lecture notes and background readings. This is necessary, because the ranking of a problem depends on the material that students have encountered in class previously. The faculty members independently ranked each problem. Interrater reliability of independent rankings was determined using an intraclass coefficient (0.82). The faculty members met to discuss their rankings and settled disagreements by consensus. The preliminary Bloom’s rankings and the faculty consensus Bloom’s rankings for problems are reported in Table 1 . For the remainder of the paper, we use the consensus Bloom’s rankings to describe problems as either lower order or higher order.

Administration of Problems to Students.

The 13 problems included in this study were administered to students on exams 1, 2, 3, and the final exam as follows: three on exam 1, three on exam 2 four on exam 3, and three on the final exam. Students’ multiple-choice responses were part of the actual exam score. They received 0.5 extra-credit points for providing satisfactory documentation of their thought processes. Students did not receive extra credit if we judged their documentation to be insufficient. Insufficient responses were those in which students made only one or two brief statements about their problem-solving process (e.g., “I chose C”). Students could answer the multiple-choice problem and opt not to provide documentation of their thinking for extra credit. Students could receive up to 6.5 points of extra credit for documentation of the problem set. The total points possible for the semester were 500, so extra credit for this research could account for up to 1.3% of a student’s grade.

Written Think-Alouds.

We developed a protocol to capture students’ written descriptions of their thought processes while solving problems on exams based on a think-aloud interview approach. In the think-aloud interview approach, research participants are given a problem to solve and are asked to say aloud everything they are thinking while solving the problem ( Ericsson and Simon, 1984 ; Keys, 2000 ). In the written think-aloud, students are asked to write, rather than say aloud, what they are thinking as they solve a problem. To train students to perform a written think-aloud, the course instructor modeled the think-aloud in class. She then assigned a homework problem that required students to answer a multiple-choice problem and construct written think-alouds recounting how they solved the problem. We then reviewed students’ homework and provided feedback. We selected examples of good documentation and poor documentation and published these anonymously on the online course-management system. After this training and feedback, we included four problems on every exam for which we asked students to provide a written think-aloud description. We collected 1087 written think-alouds from 140 students (63% of course enrollment, n = 222) for 13 problems. Figure 2 shows a typical example of a student written think-aloud.

Written think-aloud from an introductory biology student who had been instructed to write down her procedures for solving a multiple-choice biology problem. This document describes the student’s procedures for solving the problem shown in Figure 1 .

Data Analysis

We analyzed students’ written think-alouds using a combination of qualitative and quantitative methods. We used qualitative content analysis ( Patton, 1990 ) to identify and categorize the primary patterns of student thinking during problem solving. We used quantitative analysis to determine the relationship between use of domain-general, hybrid, and domain-specific procedures and problem type and to investigate the impact of domain-general/hybrid and domain-specific procedure use on answering correctly.

Qualitative Analyses of Students’ Written Think-alouds.

The goal of our qualitative analysis was to identify the cognitive procedures students follow to solve multiple-choice biology problems during an exam. Our qualitative analysis took place in two phases.

Phase 1: Establishing Categories of Student Problem-Solving Procedures.

Independently, we read dozens of individual think-alouds for each problem. While we read, we made notes about the types of procedures we observed. One author (P.P.L.) noted, for example, that students recalled concepts, organized their thinking, read and ruled out multiple-choice options, explained their selections, and weighed the pros and cons of multiple-choice options. The other author (L.B.P.) noted that students recalled theories, interpreted a phylogenetic tree, identified incomplete information, and refuted incorrect information. After independently reviewing the written think-alouds, we met to discuss what we had found and to build an initial list of categories of problem-solving procedures. Based on our discussion, we built a master list of categories of procedures (Supplemental Table S1).

Next, we compared our list with Bloom’s Taxonomy of Educational Objectives ( Anderson and Krathwohl, 2001 ) and the Blooming Biology Tool ( Crowe et al. , 2008 ). We sought to determine whether the cognitive processes described in these sources corresponded to the cognitive processes we observed in our initial review of students’ written think-alouds. Where there was overlap, we renamed our categories to use the language of Bloom’s taxonomy. For the categories that did not overlap, we kept our original names.

Phase 2: Assigning Student Problem-Solving Procedures to Categories.

Using the list of categories developed in phase 1, we categorized every problem-solving procedure articulated by students in the written think-alouds. We analyzed 1087 documents for 13 problems. For each of the 13 problems, we followed the same categorization process. In a one-on-one meeting, we discussed a few written think-alouds. While still in the same room, we categorized several written think-alouds independently. We then compared our categorizations and discussed any disagreements. We then repeated these steps for additional think-alouds while still together. Once we reached agreement on all categories for a single problem, we independently categorized a common subset of written think-alouds to determine interrater reliability. When interrater reliability was below a level we considered acceptable (0.8 Cronbach’s alpha), we went through the process again. Then one author (either L.B.P. or P.P.L.) categorized the remainder of the written think-alouds for that problem.

At the end of phase 2, after we had categorized all 1087 written think-alouds, we refined our category list, removing categories with extremely low frequencies and grouping closely related categories. For example, we combined the category Executing with Implementing into a category called Analyzing Visual Representations.

Phase 3: Aligning Categories with Our Theoretical Framework.

Having assigned student problem-solving procedures to categories, we determined whether the category aligned best with domain-general or domain-specific problem solving. To make this determination, we considered the extent to which the problem-solving procedures in a category depended on knowledge of biology. Categories of procedures aligned with domain-general problem solving were carried out without drawing on content knowledge (e.g., Clarifying). Categories aligned with domain-specific problem solving were carried out using content knowledge (e.g., Checking). We also identified two categories of problem solving that we labeled hybrids of domain-general and domain-specific problem solving, because students used content knowledge in these steps, but they did so superficially (e.g., Recognizing).

Supplemental Table S1 shows the categories that resulted from our analytical process, including phase 1 notes, phase 2 categories, and phase 3 final category names as presented in this paper. Categories are organized into the themes of domain-general, hybrid, and domain-specific problem solving (Supplemental Table S1).

Quantitative Analyses of Students’ Written Think-Alouds.

To determine whether students used domain-general/hybrid or domain-specific problem solving preferentially when solving problems ranked by faculty as lower order or higher order, we used generalized linear mixed models (GLMM). GLMM are similar to ordinary linear regressions but take into account nonnormal distributions. GLMM can also be applied to unbalanced repeated measures ( Fitzmaurice et al. , 2011 ). In our data set, an individual student could provide documentation to one or more problems (up to 13 problems). Thus, in some but not all cases, we have repeated measures for individuals. To account for these repeated measures, we used “student” as our random factor. We used the problem type (lower order or higher order) as our fixed factor. Because our independent variables, number of domain-general/hybrid procedures and number of domain-specific procedures, are counts, we used a negative binomial regression. For this analysis and subsequent quantitative analyses, we grouped domain-general and hybrid procedures. Even though hybrid procedures involve some use of content knowledge, the content knowledge is used superficially; we specifically wanted to investigate the impact of weak content-knowledge use compared with strong content-knowledge use. Additionally, the number of hybrid procedures in our data set is relatively low compared with domain-general and domain-specific.

To determine whether students who used more domain-general/hybrid procedures or domain-specific procedures were more likely to have correct answers to the problems, we also used GLMM. We used the number of domain-general/hybrid procedures and the number of domain-specific procedures as our fixed factors and student as our random factor. In this analysis, our dependent variable (correct or incorrect response) was dichotomous, so we used a logistic regression ( Fitzmaurice et al. , 2011 ). We also explored the correlations between the average number of domain-general/hybrid and domain-specific procedures used by students and their final percentage of points for the course.

In this section, we present the results of our analyses of students’ procedures while solving 13 multiple-choice, biology problems ( Figure 1 and Supplemental Figure S1). We used the written think-aloud protocol to discover students’ problem-solving procedures for all 13 problems.

Students Use Domain-General and Domain-Specific Procedures to Solve Multiple-Choice Biology Problems

We identified several categories of procedures practiced by students during problem solving, and we organized these categories based on the extent to which they drew upon knowledge of biology. Domain-general procedures do not depend on biology content knowledge. These procedures also could be used in other domains. Hybrid procedures show students assessing multiple-choice options with limited and superficial references to biology content knowledge. Domain-specific procedures depend on biology content knowledge and reveal students’ retrieval and processing of correct ideas about biology.

Domain-General Procedures.

We identified five domain-general problem-solving procedures that students practiced ( Table 2 ). Three of these have been described in Bloom’s taxonomy ( Anderson and Krathwohl, 2001 ). These include Analyzing Domain-General Visual Representations, Clarifying, and Comparing Language of Options. In addition, we discovered two other procedures, Correcting and Delaying, that we also categorized as domain general ( Table 2 ).

Students’ problem-solving procedures while solving multiple-choice biology problems

The procedures are categorized as domain-general, hybrid, and domain-specific. Superscripts indicate whether the problem-solving procedure aligns with previously published conceptions of student thinking or was newly identified in this study: a , Anderson and Krathwohl (2001) ; b identified in this study; c , Crowe et al . (2008) .

During Correcting, students practiced metacognition. Broadly defined, metacognition occurs when someone knows, is aware of, or monitors his or her own learning ( White, 1998 ). When students corrected, they identified incorrect thinking they had displayed earlier in their written think-aloud and mentioned the correct way of thinking about the problem.

When students Delayed, they described their decision to postpone full consideration of one multiple-choice option until they considered other multiple-choice options. We interpreted these decisions as students either not remembering how the option connected with the question or not being able to connect that option to the question well enough to decide whether it could be the right answer.

Hybrid Procedures.

We identified two problem-solving procedures that we categorized as hybrid, Comparing Correctness of Options and Recognizing. Students who compared correctness of options stated that one choice appeared more correct than the other without giving content-supported reasoning for their choice. Similarly, students who recognized an option as correct did not support this conclusion with a content-based rationale.

Domain-Specific Procedures.

In our data set, we identified six domain-specific problem-solving procedures practiced by students ( Table 2 ). Four of these have been previously described. Specifically, Analyzing Domain-Specific Visual Representations, Checking, and Recalling were described in Bloom’s taxonomy ( Anderson and Krathwohl, 2001 ). Predicting was described by Crowe and colleagues (2008) . We identified two additional categories of domain-specific problem-solving procedures practiced by students who completed our problem set, Adding Information and Asking a Question.

Adding Information occurred when students recalled material that was pertinent to one of the multiple-choice options and incorporated that information into their explanations of why a particular option was wrong or right.

Asking a Question provides another illustration of students practicing metacognition. When students asked a question, they pointed out that they needed to know some specific piece of content that they did not know yet. Typically, students who asked a question did so repeatedly in a single written think-aloud.

Students Make Errors While Solving Multiple-Choice Biology Problems

In addition to identifying domain-general, hybrid, and domain-general procedures that supported students’ problem-solving, we identified errors in students’ problem solving. We observed six categories of errors, including four that we categorized as domain general and two categorized as domain specific ( Table 3 ).

Students’ errors while solving multiple-choice biology problems

The errors are presented in alphabetical order, described, and illustrated with example quotes from different students’ documentation of their solutions to the problem shown in Figure 1 (except for Misreading, which is from problem 13 in Supplemental Figure S1).

The domain-general errors include Contradicting, Disregarding Evidence, Misreading, and Opinion-Based Judgment. In some cases, students made statements that they later contradicted; we called this Contradicting. Disregarding Evidence occurred when students’ failed to indicate use of evidence. Several problems included data in the question prompt or in visual representations. These data could be used to help students select the best multiple-choice option, yet many students gave no indication that they considered these data. When students’ words led us to believe that they did not examine the data, we assigned the category Disregarding Evidence.

Students also misread the prompt or the multiple-choice options, and we termed this Misreading. For example, Table 3 shows the student Misreading; the student states that Atlantic eels are in the presence of krait toxins, whereas the question prompt stated there are no krait in the Atlantic Ocean. In other cases, students stated that they arrived at a decision based on a feeling or because that option just seemed right. For example, in selecting option C for the stickleback problem ( Figure 1 ), one student said, “E may be right, but I feel confident with C. I chose Answer C.” These procedures were coded as Opinion-Based Judgment.

We identified two additional errors that we classified as domain specific, Making Incorrect Assumptions and Misunderstanding Content. Making Incorrect Assumptions was identified when students made faulty assumptions about the information provided in the prompt. In these cases, students demonstrated in one part of their written think-aloud that they understood the conditions for or components of a concept. However, in another part of the written think-aloud, students assumed the presence or absence of these conditions or components without carefully examining whether they held for the given problem. In the example shown in Table 3 , the student assumed additional information on fertility that was not provided in the problem.

We classified errors that showed a poor understanding of the biology content as Misunderstanding Content. Misunderstanding Content was exhibited when students stated incorrect facts from their long-term memory, made false connections between the material presented and biology concepts, or showed gaps in their understanding of a concept. In the Misunderstanding Content example shown in Table 3 , the student did not understand that the biological species concept requires two conditions, that is, the offspring must be viable and fertile. The student selected the biological species concept based only on evidence of viability, demonstrating misunderstanding.

To illustrate the problem-solving procedures described above, we present three student written think-alouds ( Table 4, A–C ). All three think-alouds were generated in response to the stickleback problem; pseudonyms are used to protect students’ identities ( Figure 1 ). Emily correctly solved the stickleback problem using a combination of domain-general and domain-specific procedures ( Table 4A ). She started by thinking about the type of answer she was looking for (Predicting). Then she analyzed the stickleback drawings and population table (Analyzing Domain-General Visual Representations) and explained why options were incorrect or correct based on her knowledge of species concepts (Checking). Brian ( Table 4B ) took an approach that included domain-general and hybrid procedures. He also made some domain-general and domain-specific errors, which resulted in an incorrect answer; Brian analyzed some of the domain-general visual representations presented in the problem but disregarded others. He misunderstood the content, incorrectly accepting the biological species concept. He also demonstrated Recognizing when he correctly eliminated choice B without giving a rationale for this step. In our third example ( Table 4C ), Jessica used domain-general, hybrid, and domain-specific procedures, along with a domain-specific error, and arrived at an incorrect answer.

Students’ written think-alouds describing their processes for solving the stickleback problem

Different types of problem-solving processes are indicated with different font types: Domain-general problem-solving steps: blue lowercase font; domain-specific problem-solving steps: blue uppercase font, hybrid problem-solving steps: blue italics; domain-general errors: orange lowercase font; domain-specific errors: orange uppercase font. The written think-alouds are presented in the exact words of the students. A, Emily, all domain-general and domain-specific steps; correct answer: E; B, Brian, domain-general and hybrid steps, domain-general and domain-specific errors; incorrect answer: C; C, Jessica, domain-general, hybrid, and domain-specific steps; domain-specific errors; incorrect answer: C.

Domain-Specific Procedures Are Used More Frequently for Higher-Order Problems Than Lower-Order Problems

To determine the extent to which students use domain-general and domain-specific procedures when solving lower-order versus higher-order problems, we determined the frequency of domain-general and hybrid procedures and domain-specific procedures for problems categorized by experts as lower order or higher order. We grouped domain-general and hybrid procedures, because we specifically wanted to examine the difference between weak and strong content usage. As Table 5, A and B , shows, students frequently used both domain-general/hybrid and domain-specific procedures to solve all problems. For domain-general/hybrid procedures, by far the most frequently used procedure for lower-order problems was Recognizing ( n = 413); the two most frequently used procedures for higher-order problems were Analyzing Domain-General Representations ( n = 153) and Recognizing ( n = 105; Table 5A ). For domain-specific procedures, the use of Checking dominated both lower-order ( n = 903) and higher-order problems ( n = 779). Recalling also was used relatively frequently for lower-order problems ( n = 207), as were Analyzing Domain-Specific Visual Representations, Predicting, and Recalling for higher-order problems ( n = 120, n = 106, and n = 107, respectively). Overall, students used more domain-general and hybrid procedures when solving lower-order problems (1.43 ± 1.348 per problem) than when solving higher-order problems (0.74 ± 1.024 per problem; binomial regression B = 0.566, SE = 0.079, p < 0.005). Students used more domain-specific procedures when solving higher-order problems (2.57 ± 1.786 per problem) than when solving lower-order problems (2.38 ± 2.2127 per problem; binomial regression B = 0.112, SE = 0.056, p < 0.001).

Frequency of each problem-solving procedure for lower-order and higher-order problems

Procedures are presented from left to right in alphabetical order. A color scale is used to represent the frequency of each procedure, with the lowest-frequency procedures shown in dark blue, moderate-frequency procedures shown in white, and high-frequency procedures shown in dark red.

Most Problem-Solving Errors Made by Students Involve Misunderstanding Content

We also considered the frequency of problem-solving errors made by students solving lower-order and higher-order problems. As Table 6 shows, most errors were categorized with the domain-specific category Misunderstanding Content, and this occurred with about equal frequency in lower-order and higher-order problems. The other categories of errors were less frequent. Interestingly, the domain-general errors Contradicting and Opinion-Based Judgment both occurred more frequently with lower-order problems. In contrast, the domain-specific error Making Incorrect Assumptions occurred more frequently with higher-order problems.

Frequency of errors for lower-order and higher-order problems

Categories of errors are presented from left to right in alphabetical order. A color scale is used to represent the frequency of each type of error, with the lowest-frequency errors shown in dark blue, moderate-frequency errors shown in white, and high-frequency errors shown in dark red.

Using Multiple Domain-Specific Procedures Increases the Likelihood of Answering a Problem Correctly

To examine the extent to which the use of domain-general or domain-specific procedures influences the probability of answering problems correctly, we performed a logistic regression. Predicted probabilities of answering correctly are shown in Figure 3 for domain-general and hybrid procedures and Figure 4 for domain-specific procedures. Coefficients of the logistic regression analyses are presented in Supplemental Tables S2 and S3. As Figure 3 shows, using zero domain-general or hybrid procedures was associated with a 0.53 predicted probability of being correct. Using one domain-general or hybrid procedure instead of zero increased the predicted probability of correctly answering a problem to 0.79. However, students who used two or more domain-general or hybrid procedures instead of one did not increase the predicted probability of answering a problem correctly. In contrast, as Figure 4 shows, using zero domain-specific procedures was associated with only a 0.34 predicted probability of answering the problem correctly, and students who used one domain-specific procedure had a 0.54 predicted probability of success. Strikingly, the more domain-specific procedures used by students, the more likely they were to answer a problem correctly up to five procedures; students who used five domain-specific procedures had a 0.97 probability of answering correctly. Predicted probabilities for students using seven and nine domain-specific codes show large confidence intervals around the predictions due to the low sample size ( n = 8 and 4, respectively). Also, we examined the extent to which the use of domain-general or domain-specific procedures correlates with course performance. We observed a weak positive correlation between the average number of domain-specific procedures used by students for a problem and their final percentage of points in the course (Spearman’s rho = 0.306; p < 0.001). There was no correlation between the average number of domain-general/hybrid procedures used by students for a problem and their final percentage of points in the course (Spearman’s rho = 0.015; p = 0.857).

Predicted probability of a correct answer based on the number of domain-general and hybrid procedures.

Predicted probability of a correct answer based on the number of domain-specific procedures.

We have used the theoretical framework of domain-specific problem solving to investigate student cognition during problem solving of multiple-choice biology problems about ecology, evolution, and systems biology. Previously, research exploring undergraduate cognition during problem solving has focused on problem categorization or students’ solutions to open-response problems ( Smith and Good, 1984 ; Smith, 1988 ; Lavoie, 1993 ; Nehm and Ridgway, 2011 ; Smith et al. 2013 ). Our goal was to describe students’ procedural knowledge, including the errors they made in their procedures. Below we draw several important conclusions from our findings and consider the implications of this research for teaching and learning.

Domain-Specific Problem Solving Should Be Used for Innovative Investigations of Biology Problem Solving

Students in our study used a variety of procedures to solve multiple-choice biology problems, but only a few procedures were used at high frequency, such as Recognizing and Checking. Other procedures that biology educators might most want students to employ were used relatively infrequently, including Correcting and Predicting. Still other procedures that we expected to find in our data set were all but absent, such as Stating Assumptions. Our research uncovers the range of procedures promoted by multiple-choice assessment in biology. Our research also provides evidence for the notion that multiple-choice assessments are limited in their ability to prompt some of the critical types of thinking used by biologists.

We propose that our categorization scheme and the theoretical framework of domain-specific problem solving should be applied for further study of biology problem solving. Future studies could be done to understand whether different ways of asking students to solve a problem at the same Bloom’s level could stimulate students to use different procedures. For example, if the stickleback problem ( Figure 1 ) were instead presented to students as a two-tier multiple-choice problem, as multiple true–false statements, or as a constructed-response problem, how would students’ procedures differ? Additionally, it would be useful to investigate whether the more highly desired, but less often observed procedures of Correcting and Predicting are used more frequently in upper-level biology courses and among more advanced biology students.

We also propose research to study the interaction between procedure and content. With our focus on procedural knowledge, we intentionally avoided an analysis of students’ declarative knowledge. However, our process of analysis led us to the conclusion that our framework can be expanded for even more fruitful research. For example, one could look within the procedural category Checking to identify the declarative knowledge being accessed. Of all the relevant declarative knowledge for a particular problem, which pieces do students typically access and which pieces are typically overlooked? The answer to this question may tell us that, while students are using an important domain-specific procedure, they struggle to apply a particular piece of declarative knowledge. As another example, one could look within the procedural category Analyzing Visual Representations to identify aspects of the visual representation that confuse or elude students. Findings from this type of research would show us how to modify visual representations for clarity or how to scaffold instruction for improved learning. We are suggesting that future concurrent studies of declarative and procedural knowledge will reveal aspects of student cognition that will stay hidden if these two types of knowledge are studied separately. Indeed, problem-solving researchers have investigated these types of interactions in the area of comprehension of science textbooks ( Alexander and Kulikowich, 1991 , 1994 ).

Lower-Order Problems May Not Require Content Knowledge, While Higher-Order Problems Promote Strong Content Usage

Because of the pervasive use among biology educators of Bloom’s taxonomy to write and evaluate multiple-choice assessments, we decided it was valuable to examine the relationship between domain-general and domain-specific procedures and lower-order versus higher-order problems.

For both lower-order and higher-order problems, domain-specific procedures were used much more frequently than domain-general procedures ( Table 5, A and B ). This is comforting and unsurprising. We administered problems about ecosystems, evolution, and structure–function relationships, so we expected and hoped students would use their knowledge of biology to solve these problems. However, two other results strike us as particularly important. First, domain-general procedures are highly prevalent ( Table 5A , n = 1108 across all problems). The use of domain-general procedures is expected. There are certain procedures that are good practice in problem solving regardless of content, such as Analyzing Domain-General Visual Representations and Clarifying. However, students’ extensive use of other domain-general/hybrid categories, namely Recognizing, is disturbing. Here we see students doing what all biology educators who use multiple-choice assessment fear, scanning the options for one that looks right based on limited knowledge. It is even more concerning that students’ use of Recognizing is nearly four times more prevalent in lower-order problems than higher-order problems and that overall domain-general procedures are more prevalent in lower-order problems ( Table 5A ). As researchers have discovered, lower-order problems, not higher-order problems, are the type most often found in college biology courses ( Momsen et al ., 2010 ). That means biology instructors’ overreliance on lower-order assessment is likely contributing to students’ overreliance on procedures that do not require biology content knowledge.

Second, it is striking that domain-specific procedures are more prevalent among higher-order problems than lower-order problems. These data suggest that higher-order problems promote strong content usage by students. As others have argued, higher-order problems should be used in class and on exams more frequently ( Crowe et al. , 2008 ; Momsen et al. , 2010 ).

Using Domain-Specific Procedures May Improve Student Performance

Although it is interesting in and of itself to learn the procedures used by students during multiple-choice assessment, the description of these categories of procedures begs the question: does the type of procedure used by students make any difference in their ability to choose a correct answer? As explained in the Introduction , the strongest problem-solving approaches stem from a relatively complete and well-organized knowledge base within a domain ( Chase and Simon, 1973 ; Chi et al. , 1981 ; Pressley et al. , 1987 ; Alexander and Judy, 1998). Thus, we hypothesized that use of domain-specific procedures would be associated with solving problems correctly, but use of domain-general procedures would not. Indeed, our data support this hypothesis. While limited use of domain-general procedures was associated with improved probability of success in solving multiple-choice problems, students who practiced extensive domain-specific procedures almost guaranteed themselves success in multiple-choice problem solving. In addition, as students used more domain-specific procedures, there was a weak but positive increase in the course performance, while use of domain-general procedures showed no correlation to performance. These data reiterate the conclusions of prior research that successful problem solvers connect information provided within the problem to their relatively strong domain-specific knowledge ( Smith and Good, 1984 ; Pressley et al. , 1987 ). In contrast, unsuccessful problem solvers heavily depend on relatively weak domain-specific knowledge ( Smith and Good, 1984 ; Smith, 1988 ). General problem-solving procedures can be used to make some progress in reaching a solution to domain-specific problems, but a problem solver can get only so far with this type of thinking. In solving domain-specific problems, at some point, the solver has to understand the particulars of a domain to reach a legitimate solution (reviewed in Pressley et al. , 1987 ; Bassok and Novick, 2012 ). Likewise, problem solvers who misunderstand key conceptual pieces or cannot identify the deep, salient features of a problem will generate inadequate, incomplete, or faulty solutions ( Chi et al. , 1981 ; Nehm and Ridgway, 2011 ).

Our findings strengthen the conclusions of previous work in two important ways. First, we studied problems from a wider range of biology topics. Second, we studied a larger population of students, which allowed us to use both qualitative and quantitative methods.

Limitations of This Research

Think-aloud protocols typically take place in an interview setting in which students verbally articulate their thought processes while solving a problem. When students are silent, the interviewer is there to prompt them to continue thinking aloud. We modified this protocol and taught students how to write out their procedures. However, one limitation of this study and all think-aloud studies is that it is not possible to analyze what students may have been thinking but did not state. Despite this limitation, we were able to identify a range of problem-solving procedures and errors that inform teaching and learning.

Implications for Teaching and Learning

There is general consensus among biology faculty that students need to develop problem-solving skills ( NRC, 2003 ; AAAS, 2011 ). However, problem solving is not intuitive to students, and these skills typically are not explicitly taught in the classroom ( Nehm, 2010 ; Hoskinson et al. , 2013 ). One reason for this misalignment between faculty values and their teaching practice is that biology problem-solving procedures have not been clearly defined. Our research presents a categorization of problem-solving procedures that faculty can use in their teaching. Instructors can use these well-defined problem-solving procedures to help students manage their knowledge of biology; students can be taught when and how to apply knowledge and how to restructure it. This gives students the tools to become more independent problem solvers ( Nehm, 2010 ).

We envision at least three ways that faculty can encourage students to become independent problem solvers. First, faculty can model the use of problem-solving procedures described in this paper and have students write out their procedures, which makes them explicit to both the students and instructor. Second, models should focus on domain-specific procedures, because these steps improve performance. Explicit modeling of domain-specific procedures would be eye-opening for students, who tend to think that studying for recognition is sufficient, particularly for multiple-choice assessment. However, our data and those of other researchers ( Stanger-Hall, 2012 ) suggest that studying for and working through problems using strong domain-specific knowledge can improve performance, even on multiple-choice tests. Third, faculty should shift from the current predominant use of lower-order problems ( Momsen et al. , 2010 ) toward the use of more higher-order problems. Our data show that lower-order problems prompt for domain-general problem solving, while higher-order problems prompt for domain-specific problem solving.

We took what we learned from the investigation reported here and applied it to develop an online tutorial called SOLVEIT for undergraduate biology students ( Kim et al. , 2015 ). In SOLVEIT, students are presented with problems similar to the stickleback problem shown in Figure 1 . The problems focus on species concepts and ecological relationships. In brief, SOLVEIT asks students to provide an initial solution to each problem, and then it guides students through the problem in a step-by-step manner that encourages them to practice several of the problem-solving procedures reported here, such as Recalling, Checking, Analyzing Visual Representations, and Correcting. In the final stages of SOLVEIT, students are asked to revise their initial solutions and to reflect on an expert’s solution as well as their own problem-solving process ( Kim et al. , 2015 ). Our findings of improved student learning with SOLVEIT ( Kim et al. , 2015 ) are consistent with the research of others that shows scaffolding can improve student problem solving ( Lin and Lehman, 1999 ; Belland, 2010 ; Singh and Haileselassie, 2010 ). Thus, research to uncover the difficulties of students during problem solving can be directly applied to improve student learning.

Supplementary Material

Acknowledgments.

We thank the students who participated in this study and the biology faculty who served as experts by providing Bloom’s rankings for each problem. We also thank the Biology Education Research Group at UGA, who improved the quality of this work with critical feedback on the manuscript. Finally, we thank the reviewers, whose feedback greatly improved the manuscript. Resources for this research were provided by UGA and the UGA Office of STEM Education.

• Alexander PA, Judy JE. The interaction of domain-specific and strategic knowledge and academic performance. Rev Educ Res. 1988; 58 :375–404. [ Google Scholar ]
• Alexander PA, Kulikowich JM. Domain-specific and strategic knowledge as predictors of expository text comprehension. J Reading Behav. 1991; 23 :165–190. [ Google Scholar ]
• Alexander PA, Kulikowich JM. Learning from physics text: a synthesis of recent research. J Res Sci Teach. 1994; 31 :895–911. [ Google Scholar ]
• American Association for the Advancement of Science. Vision and Change in Undergraduate Biology Education: A Call to Action. Washington, DC: 2011. [ Google Scholar ]
• Anderson C, Sheldon TH, Dubay J. The effects of instruction on college non-majors’ conceptions of respiration and photosynthesis. J Res Sci Teach. 1990; 27 :761–776. [ Google Scholar ]
• Anderson LW, Krathwohl DR. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Boston, MA: Allyn & Bacon; 2001. [ Google Scholar ]
• Anderson TR, Schönborn KJ, du Plessis L, Gupthar AS, Hull TL. Identifying and developing students’ ability to reason with concepts and representations in biology. In: Treagust DF, Tsui C, editors. Multiple Representations in Biological Education, vol. 7. Dordrecht, Netherlands: Springer; 2012. pp. 19–38. [ Google Scholar ]
• Bassok M, Novick LR. Problem solving. In: Holyoak KJ, Morrison RG, editors. Oxford Handbook of Thinking and Reasoning. New York: Oxford University Press; 2012. pp. 413–432. [ Google Scholar ]
• Belland BR. Portraits of middle school students constructing evidence-based arguments during problem-based learning: the impact of computer-based scaffolds. Educ Technol Res Dev. 2010; 58 :285–309. [ Google Scholar ]
• Bissell AN, Lemons PP. A new method for assessing critical thinking in the classroom. BioScience. 2006; 56 :66–72. [ Google Scholar ]
• Bloom BS. Taxonomy of Educational Objectives: The Classification of Educational Goals. New York: McKay; 1956. [ Google Scholar ]
• Brown AL. Knowing when, where, and how to remember: a problem of metacognition. In: Glaser R, editor. Advances in Instructional Psychology, vol. 1. Hillsdale, NJ: Erlbaum; 1978. pp. 77–165. [ Google Scholar ]
• Brownell SE, Wenderoth MP, Theobald R, Okoroafor N, Koval M, Freeman S, Walcher-Chevillet CL, Crowe AJ. How students think about experimental design: novel conceptions revealed by in-class activities. BioScience. 2014; 64 :125–137. [ Google Scholar ]
• Bunce DM, Gabel DL, Samuel JV. Enhancing chemistry problem-solving achievement using problem categorization. J Res Sci Teach. 1991; 28 :505–521. [ Google Scholar ]
• Cartrette DP, Bodner GM. Non-mathematical problem solving in organic chemistry. J Res Sci Teach. 2010; 47 :643–660. [ Google Scholar ]
• Chase WG, Simon HA. The mind’s eye in chess. In: Chase WG, editor. Visual Information Processing. New York: Academic; 1973. pp. 115–181. [ Google Scholar ]
• Chi MTH, Feltovich PJ, Glaser R. Categorization and representation of physics problems by experts and novices. Cogn Sci. 1981; 5 :121–152. [ Google Scholar ]
• Chi MTH, Glaser R. Problem-solving ability. In: Sternberg RJ, editor. Human Abilities: An Information-Processing Approach. New York: Freeman; 1985. [ Google Scholar ]
• Crowe A, Dirks C, Wenderoth MP. Biology in Bloom: implementing Bloom’s taxonomy to enhance student learning in biology. CBE Life Sci Educ. 2008; 7 :368–381. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dawson MRW. Understanding Cognitive Science, 1st ed. Malden, MA: Wiley-Blackwell; 1998. [ Google Scholar ]
• Dirks C, Leroy C, Wenderoth MP. Science Process and Reasoning Skills Test (SPARST): development and early diagnostic results. 2013. Presented at the Society for the Advancement of Biology Education Research (SABER) annual meeting, held 11–14 July 2013, in Minneapolis, MN.
• Duncker K, Lees LS. On problem-solving. Psychol Monogr. 1945; 58 :i–113. [ Google Scholar ]
• Ericsson KA, Simon HA. Protocol Analysis: Verbal Reports as Data, rev. ed. Cambridge, MA: MIT Press; 1984. [ Google Scholar ]
• Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis, 2nd ed. Hoboken, NJ: Wiley; 2011. [ Google Scholar ]
• Gormally C, Brickman P, Lutz M. Developing a test of scientific literacy skills (TOSLS): measuring undergraduates’ evaluations of scientific information and arguments. CBE Life Sci Educ. 2012; 11 :364–377. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Halpern DE. Critical Thinking across the Curriculum: A Brief Edition of Thought and Knowledge. Mahwah, NJ: Erlbaum; 1997. [ Google Scholar ]
• Hartley LM, Wilke BJ, Schramm JW, D’Avanzo C, Anderson CW. College students’ understanding of the carbon cycle: contrasting principle-based and informal reasoning. BioScience. 2011; 61 :65–75. [ Google Scholar ]
• Hoskinson A-M, Caballero MD, Knight JK. How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research. CBE Life Sci Educ. 2013; 12 :153–161. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jacobs JE, Paris SG. Children’s metacognition about reading—issues in definition, measurement, and instruction. Educ Psychol. 1987; 22 :255–278. [ Google Scholar ]
• Jonassen D. In: Trends and Issues in Instructional Design and Technology. 3rd ed., ed. RA Reiser and JV Dempsey, Boston, MA: Pearson Education; 2012. Designing for problem solving; pp. 64–74. [ Google Scholar ]
• Jonassen DH. Toward a design theory of problem solving. Educ Technol Res Dev. 2000; 48 :63–85. [ Google Scholar ]
• Keys CW. Investigating the thinking processes of eighth grade writers during the composition of a scientific laboratory report. J Res Sci Teach. 2000; 37 :676–690. [ Google Scholar ]
• Kim HS, Prevost L, Lemons PP. Students’ usability evaluation of a Web-based tutorial program for college biology problem solving. J Comput Assist Learn. 2015; 31 :362–377. [ Google Scholar ]
• Kohl PB, Finkelstein ND. Patterns of multiple representation use by experts and novices during physics problem solving. Phys Rev Spec Top Phys Educ Res. 2008; 4 :010111. [ Google Scholar ]
• Lavoie DR. The development, theory, and application of a cognitive-network model of prediction problem solving in biology. J Res Sci Teach. 1993; 30 :767–785. [ Google Scholar ]
• Lin X, Lehman JD. Supporting learning of variable control in a computer-based biology environment: effects of prompting college students to reflect on their own thinking. J Res Sci Teach. 1999; 36 :837–858. [ Google Scholar ]
• Martinez ME. What is problem solving. Phi Delta Kappan. 1998; 79 :605–609. [ Google Scholar ]
• Momsen J, Offerdahl E, Kryjevskaia M, Montplaisir L, Anderson E, Grosz N. Using assessments to investigate and compare the nature of learning in undergraduate science courses. CBE Life Sci Educ. 2013; 12 :239–249. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Momsen JL, Long TM, Wyse SA, Ebert-May D. Just the facts? Introductory undergraduate biology courses focus on low-level cognitive skills. CBE Life Sci Educ. 2010; 9 :435–440. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• National Research Council. BIO2010: Transforming Undergraduate Education for Future Research Biologists. Washington, DC: National Academies Press; 2003. [ PubMed ] [ Google Scholar ]
• Nehm RH. Understanding undergraduates’ problem-solving processes. J Microbiol Biol Educ. 2010; 11 :119–122. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nehm RH, Reilly L. Biology majors’ knowledge and misconceptions of natural selection. BioScience. 2007; 57 :263–272. [ Google Scholar ]
• Nehm RH, Ridgway J. What do experts and novices “see” in evolutionary problems. Evol Educ Outreach. 2011; 4 :666–679. [ Google Scholar ]
• Newell A, Shaw JC, Simon HA. Elements of a theory of human problem solving. Psychol Rev. 1958; 65 :151–166. [ Google Scholar ]
• Newell A, Simon HA. Human Problem Solving. Upper Saddle River, NJ: Prentice-Hall; 1972. [ Google Scholar ]
• Patton MQ. Qualitative Evaluation and Research Methods. Thousand Oaks, CA: Sage; 1990. [ Google Scholar ]
• Polya G. How to Solve It. Garden City, NY: Doubleday; 1957. [ Google Scholar ]
• Pressley M, Borkowski JG, Schneider W. Cognitive strategies: good strategy users coordinate metacognition and knowledge. Ann Child Dev. 1987; 4 :89–129. [ Google Scholar ]
• Pressley M, Goodchild F, Fleet J, Zajchowski R, Evans ED. The challenges of classroom strategy instruction. Elem Sch J. 1989; 89 :301–342. [ Google Scholar ]
• Runco MA, Chand I. Cognition and creativity. Educ Psychol Rev. 1995; 7 :243–267. [ Google Scholar ]
• Schraw G, Moshman D. Metacognitive theories. Educ Psychol Rev. 1995; 7 :351–371. [ Google Scholar ]
• Singer SR, Nielsen NR, Schweingburger HA, Committee on the Status, Contributions, and Future Directions of Discipline-Based Education Research . Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering. Washington, DC: National Academies Press; 2012. [ Google Scholar ]
• Singh C, Haileselassie D. Developing problem-solving skills of students taking introductory physics via Web-based tutorials. J Coll Sci Teach. 2010; 39 :42–49. [ Google Scholar ]
• Smith JI, Combs ED, Nagami PH, Alto VM, Goh HG, Gourdet MA, Hough CM, Nickell AE, Peer AG, Coley JD, et al. Development of the biology card sorting task to measure conceptual expertise in biology. CBE Life Sci Educ. 2013; 12 :628–644. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Smith MU. Successful and unsuccessful problem solving in classical genetic pedigrees. J Res Sci Teach. 1988; 25 :411–433. [ Google Scholar ]
• Smith MU. Expertise and the organization of knowledge: unexpected differences among genetic counselors, faculty, and students on problem categorization tasks. J Res Sci Teach. 1992; 29 :179–205. [ Google Scholar ]
• Smith MU, Good R. Problem solving and classical genetics: successful versus unsuccessful performance. J Res Sci Teach. 1984; 21 :895–912. [ Google Scholar ]
• Stanger-Hall KF. Multiple-choice exams: an obstacle for higher-level thinking in introductory science classes. CBE Life Sci Educ. 2012; 11 :294–306. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• White RT. Decisions and problems in research on metacognition. In: Fraser B, Tobin KG, editors. International Handbook of Science Education. Dordrecht, Netherlands: Springer; 1998. pp. 1207–1213. [ Google Scholar ]
• Zheng AY, Lawhorn JK, Lumley T, Freeman S. Application of Bloom’s taxonomy debunks the “MCAT myth.” Science. 2008; 319 :414–415. [ PubMed ] [ Google Scholar ]
• Zoller U. Are lecture and learning compatible? Maybe for LOCS: unlikely for HOCS. J Chem Educ. 1993; 70 :195–197. [ Google Scholar ]