Problem-Solving in Education
The world has taken drastic shifts over the past several decades, including a surge in technology advances, and a job market that seems to swirl as quickly as the weather. The education system, which tends to operate about 20 years behind research, is already behind, with school districts taking hard looks at how to move archaic curriculum and standards into meaningly knowledge and skills that will prepare students, no longer for one career, but rather to be skillful and marketable as they prepare for jobs that do not yet exist. In other words, we no longer need to fill children’s heads with facts and figures, but rather, we need to teach children how to think, how to think about thinking (i.e. metacognition), and how to problem-solve.
In a study of problem solving as involved in computer science, Douglas Lecorchick, Scott Nichols, and Lauren Tabor developed a problem-solving archetype (Lecorchick, 2020). In it, they suggest that one should begin the problem-solving process by identifying the Unmet Need, the Current State, and the Desired State. The process then takes a deeper dive into the Unmet Need, further identifying requirements, existing constraints, anticipated constraints, need setting, and existing systems in use. An idea is then proposed and evaluated, including constraints, implementation procedure, modification, and sub-problems det
The Mental Processes of Problem-Solving and Computational Thinking
Problem-solving by itself is a vast concept that we engage in on a daily basis. It can be as simple as choosing whether to purchase Fuji or honeycrisp apples (always honeycrisp, by the way) and as complex as designing rocket propulsion systems. This topic hits very close to home for me right now, as I have found my own children are struggling to see a variety of solutions to a given problem. For example, my 11-year-old son had an award-winning meltdown a few days ago when his dental appointment for some fillings had to be rescheduled. In his mind, that one appointment was the only thing standing between healthy teeth and rotted teeth crumbling from his mouth. After getting him to calm down, I explained that the appointment had been rescheduled by the dentist and that they assured me he had more time to get these fillings taken care of. I also explained that we found a better time for the appointment that worked better for our schedules. Helping him to see that there had been more options, and we chose an improved option cleared his anxiety over the situation.
Teaching our children (and even ourselves) that a given problem has infinite possible answers, is a critical concept to their development and preparation for adulthood. Problem solving includes some very basic steps: identifying the problem, coming up with possible solutions, choosing a solution, and implementing the solution.
Mustafa Yağcı did a wonderful job describing the natural process a person traverses upon encountering a problem (Yağcı, 2018). He suggests that first, a person searches for clues using their senses. Next, the person attaches meaning and interpretation to the gathered data. Then possible behavior reactions are given consideration and a behavior is selected. Last, this solution is implemented.
As a subcategorization of problem solving, computational thinking has a similar process. Paul Main did a great job of breaking computational thinking down into what he terms as 4 Cornerstones: decomposition, pattern recognition, abstraction, and algorithmic thinking.
Case-based learning I sat in 5th grade math class a couple of years ago, in which the students were learning how to convert fractions. As part of this learning, they were presented a story problem in which they had to convert a measurement from 8ths of inches to 4ths of inches. This problem could easily have been tweaked to become a case-based learning problem. Given a simple situation, the students would be able to collaboratively develop both a process and a solution.
Since computational thinking fundamentally involves real-world problem solving, case-based learning can be an effective strategy to build skills in walking through the basic process of problem identification, determining possible solutions, choosing, and testing a solution.
Problem-Based Learning (PBL) Interestingly, depending on who you ask, the educational acronym PBL is utilized to mean both problem-based learning and project-based learning. At first I thought this may have been limited to my own experiences, but Utah Valley University’s Office of Teaching and Learning found the confusion concrete enough to write a post and a video about this. They also utilized the acronym x-BL for problem-based learning, but a google search revealed that this acronym is already in use for several non-educational areas and may not be the best alternative.
In reality of implementation, it has been my experience that in both types of PBL, a problem is solved via a project, so perhaps the specifics of the nomenclature is irrelevant. In any case, problem-based learning is a wonderful way to incorporate real-world problems directly into the curriculum. In a post by McGraw Hill publishing, they outline four fundamental principles of PBLs: constructive, self-directed, collaborative, and contextual. They further describe the benefits that in PBLs, students can apply abstract ideas into practical situations.
This is a tool that can be taught independently but then threaded through every class, content, and age level. The idea is to teach students to verbalize their thought processes as they work through a problem. This process teaches reflection, methodical decision-making, and facilitates metacognition. It also allows teachers to jump in, either to add reflection or act as a guide. A specific technique, Think-aloud-Pair Problem Solving (TAPPS) actually focuses on students taking turns problem-solving aloud, while the peer listens and provides feedback.
For computational thinking, the think-aloud protocol could be implemented in tandem with case-based learning as an exercise in verbalizing thought processes and peer reflection.
Lecorchick, D., Nichols, S., & Tabor, L. (2020). Problem solving archetype – computer science.
Procedia Computer Science, 172, 655–659. https://doi.org/10.1016/j.procs.2020.05.085
Stewart, W. H., Baek, Y., Kwid, G., & Taylor, K. (2021). Exploring Factors That Influence Computational Thinking Skills in Elementary Students’ Collaborative Robotics. Journal of Educational Computing Research, 59(6), 1208-1239. https://doi-org.tcsedsystem.idm.oclc.org/10.1177/0735633121992479
Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills
. Education and Information Technologies, 24(1), 929–951. https://doi.org/10.1007/s10639-018-9801-8