Learning, Memory, and Problem-Solving of the Brain

When looking for additional resources to extend my understanding of the brain and learning as well as problem-solving methods during the learning process, I found two resources that provide not only furthered understanding but also new perspectives on these topics.

1. Do Visual, Auditory, and Kinesthetic Learners Need Visual, Auditory, and Kinesthetic Instruction? by Daniel T. Willingham: As a psychology professor at the University of Virginia as well as a member of the National Board for Education Sciences, Dr. Daniel Willingham has valuable knowledge to offer educators about the brain and learning (About, n.d.). In this article, Willingham takes the conversation past learning styles and focuses on how the way information is presented can impact the way the brain processes, stores, and later retrieves information. To begin, Willingham discusses that although learning styles assist in learning, it is really the meaning of the information that is stored and remembered. In reality, learning styles help with processing information but do little to help with storing information. This is because most topics that are taught cannot be tied as easily to visual or auditor representations. Instead, the root idea is that we want learners to remember the meaning of an idea or topic. When completing one of the most in-depth studies about learning styles and the brain, Kavale and Forness found that the use and effectiveness of learning styles was not the deciding factor. Rather, it was about teaching the content with either visual or auditory components that provided the students with the most understanding of meaning that caused the largest increase in recalling information from memory. I find this article very valuable because as a previous elementary teacher and now instructional designer, I have been taught to value and lean into every student’s learning style. However, we rarely have conversations about how the brain processes and stores that information as well as the importance of representing information in the way that the content is best analyzed and understood. Although Ormrod et al. (2009) also agree that mental imagery is an important way of understanding information, it is these mental images that create meaning that is then stored in long-term memory. Additionally, Ormrod makes the point that information is best understood when it is presented both visually and auditorily not because learners remember information as images and audio clips but because this is one of the best ways to convey the meaning that the learner needs to process and store for later use (Walden University, LLC, n.d.a). This article provides many valuable insights that take me as an instructional designer past learning styles and towards a stronger focus on how the brain process and stores the meaning of information.
2. Relationship between Brain Hemisphericity and Non-routine Problem Solving Skills of Prospective Teachers by Yeliz Yazgan and Hatice Busra Sahin: In this article, professors Yeliz Yazgan and Hatice Busra Sahin discuss the connection between the brain’s neural networks and problem solving. To be more specific, they analyze the use and dependance of the brain’s individual hemispheres in answering non-routine mathematical problems. After conducting research on this idea, results proved that both sides of the brain were activated at different times of the problem solving process, depending on the task that was given and the methods needed to reach an answer. This proved that problem solving does not rely solely on one side of the brain or the other, making the age-old claim that left-brained people are better at problem solving obsolete. This follows in line with what Ormord et. al (2009) outlined about the brain structures, in that although the left side is more dominant in details and analytics while the right side is more dominant in imagery and the unison of big pictures, both are needed to problem solve real-life situations. In addition, research found that as left-brained individuals solved problems, they followed a step-by-step written reasoning while right-brained individuals relied on pictures and graphs. This information is extremely valuable, especially as I – who is as an instructional designer – will be creating and developing math instruction that students will be learning from and interacting with. I need to think through not only how students are encoding the information they are taking in and learning (whether it be deciding what the problem is or what the solution needs to be) but also how students will be retrieving the information they already know to answer the problem (Walden University, LLC, n.d.b). With this in mind, I find this article valuable because it pushes me to evaluate how I present non-routine problem solving in a variety of formats and their solutions as well as how I ask students to respond to a problem. Acknowledging how individuals problem solve differently as well as how they respond to problems based on their brain’s function is valuable knowledge that I can carry with me as I continue my education as an instructional designer. 

References

About. Daniel Willingham--Science & Education. (n.d.). http://www.danielwillingham.com/about.html
Ormrod, J., Schunk, D., & Gredler, M. (2009). Learning theories and instruction (Laureate custom 

          edition). New York, NY: Pearson.
Walden University, LLC. (Producer). (n.d.a). Information processing and the brain [Video file]. 

          Baltimore, MD: Author.

Walden University, LLC. (Producer). (n.d.b). Information processing and problem solving [Video file]. 

          Baltimore, MD: Author.
Willingham, D. T. (n.d.). Do visual, auditory, and kinesthetic learners need visual, auditory, and 

          kinesthetic instruction? Reading Rockets. https://www.readingrockets.org/article/do-visual-

          auditory-and-kinesthetic-learners-need-visual-auditory-and-kinesthetic-instruction 

Yazgan, Y., & Sahin, H. B. (2018). Relationship between Brain Hemisphericity and Non-Routine Problem 

          Solving Skills of Prospective Teachers. Universal Journal of Educational Research, 6(9), 2001–

          2007.