Dana Bjornson (neé Allingham)
ETEC 512
University of British Columbia
Abstract
In 1999, when I started teaching full-time, I taught as many other teachers taught. Simply put, I taught the same way in which I had learned the material. After all, if the stand-and-deliver methodology worked for an average student like myself, why wouldn’t this method work for my students, as well? I did my best to have manipulatives, engaging labs, and entertaining lessons that were spiced up with occasional anecdotes and analogies, whenever I could make them work! With each year that has ticked by, my practice has evolved incrementally — that is, until now.
Keywords: Information Processing, Cognitive Neuroscience, Vygotsky.
Lesson Plan Critique:
Graphing Linear Equations Through Learning Theorist Lens
The subjects that I teach are purely academic mathematics and senior physics. These areas depend on sequential, detailed, and structured methods in problem-solving, should entrance into post-secondary, STEM-related fields be the academic pathway for a student. When I was analyzing this lesson plan, my goal was to analyze its components using the accepted learning theories, supporting Vygotsky theory about the zone of proximal development, cognitive neuroscience, and information processing, while also maintaining the necessary academic rigor that my courses demand.
Through a Vgygostkian Lens
Vygotsky believed that the learner’s spontaneous, real-life experiences anchor their non-spontaneous experiences, and that learning new material is accomplished through interacting with “More Knowledgeable Others” (Glassman, 1994). From an early age, Vygotsky himself was surrounded by numerous MKOs, thereby establishing the critical importance of sociocultural interactions in his learning theory (Pass, 1999). MKOs can help guide learners through their zone of proximal development, the zone of the learning process in which learners require assistance from an external source (John-Steiner & Mahn, 1996).
My revised lesson plan now has sociocultural interactions built into every class, which will allow either myself, more advanced students, or online programs to serve as MKOs for students in need. Vygotsky maintained that, for information to be internalized, learners must transform communicative language into inner speech and, finally, into verbal thinking (John-Steiner & Mahn, 1996). In a traditional mathematics class, there are few to no opportunities for students to interact with their MKOs; hence, the internalization process is likely not actualized during class time. The Algebra Bootcamp teams, whiteboard activity, and Google Slide collaboration activities ensure that students are no longer acting in isolation within their learning. During the work block, I will be mirroring students’ work to the entire class via Apple TV, so that MKOs can share their process with others.
Through a Neuroscience Lens
Students arriving into a typical academic Mathematics 10 class are not created equal. Each comes with a different amount of psychological “math baggage” which can negatively affect their intrinsic motivation to work diligently; moreover, their skill levels vary considerably. Thankfully, cognitive neuroscience considerations can help educators navigate through issues in their classrooms. Zamarian, Ischebeck, and Delazer point out that, with intensive practice, mathematical processes are moved from the frontal lobes of the brain responsible for “working memory” and into the left AG, where the retrieval of information is automated (2009). Taking advantage of our students’ dopamine pleasure response system can lead not only to higher levels of engagement, but ultimately to the building of skills and adaptive responses to information. As well, learning and assessment that have been chunked into challenging, yet realistic goals, will allow the teenaged brain’s desire for immediate gratification to be recognized and honoured (Willis, 2011).
Since I want to “score” with accessing my students’ dopamine reserves, I have revised my lesson plan to include the Desmos Marble Slides Activity. This online program allows students to progress incrementally to more difficult tasks with the goal of creating a slide, that falling marbles can slide down and pass through a succession of stars. The popular, interactive quizzing program Kahoot has also now been added to the lesson. In Kahoot, students can be anonymous; they can work together or individually; and they receive encouragement and praise along the way. Although I will still utilize direct instruction, this note-taking process is done using a guided, Cornell Notes system that incorporates colour, reinforcement, and social learning activities.
Through an Information Processing Lens
Humans, like a computer, require information to be received, processed, and stored, should they ever wish to retrieve that information (Orey, 2001). Although there exist many models that information processing theory has adopted, the most prevalent would be the Stage Model, whereby information may undergo a three-step process via the Sensory Register (SR), into the Short Term Memory (STM), and eventually into the Long Term Memory (LTM) (Conlan, Gallant, & Kim, 2016).
Sensory Register
Nothing beats a first impression, so say some. Intuitively, educators know that lessons that can grab a student’s attention quickly are better than those that lack any flair. The sensory register is our brain’s “first impression” receptor. All of our senses affect this register: seeing and hearing, as well as tactile, olfactory, and gustatory inputs. As information stays in the SR for only a few seconds at most, educators do not have very long to hold onto a student’s focus before the pedagogical effect is lost (Banikowski, 1999).
Initially, my lesson plan sometimes lacked a “SR-grab-you moment.” Now, each day begins with an activity that is different from the typical note-taking, followed by traditional or non-traditional reinforcement activities. Group work as a team, whiteboard activities, Desmos Marble Slides, and Kahoot all serve to enrich students’ SRs. In my experience, simply working with colourful markers on a whiteboard is engaging for students, even though it is not very “high tech.” As Kahoot seems to be taking over many classrooms at my school as a pedagogical methodology, merely hearing the theme music motivates students to participate actively in a traditionally inactive subject.
For educators, the impact of the short duration of the SR amplifies the need not to barrage students with too much information at once. Being clear about what information is important to retain is critical, as well (Banikowski, 1999). To address this notion, throughout my unit I use highlighters strategically and sparingly, and arrange the lessons so that only one or two learning outcomes are addressed per class.
Short-Term Memory
Should information enter one’s STM, one’s brain has just 15 to 30 seconds to make use of it before risking forgetfulness. Information is being actively processed in the STM, allowing us to both perceive and address stimuli during that short time span (Orey, 2001; Lutz & Huitt, 2003). The STM’s main role is to process the information for one of three purposes:
- to purge information that is not perceived as important;
- to retain information in one’s “working,” STM memory via repeated practice; or
- to transfer information to one’s LTM via rehearsal or encoding—where it is now “learned” (Banikowski, 1999).
Students with normal cognitive function require repeated actions as many as 40 times before the skill becomes automated, and thus transferred to their LTM (Banikowski, 1999). Throughout this unit, I have now provided multiple opportunities for information to be rehearsed and processed in the students’ STM, thereby increasing the likelihood of the information being stored in their LTM.
Long-Term Memory
Should information make its way into the seemingly unlimited LTM, many theorists believe that it is there for life. Sometimes the pathways leading to the information erode, making one believe that one has forgotten; however, such is likely not the case. For mathematics students, problem-solving requires the semantic declarative memory in the LTM to be activated; activation requires the linking of new ideas to pre-existing ones, in a process called “elaboration” (Orey, 2001). It has also been suggested that combining personal experiences that activate students’ episodic declarative memory will further embed information in the LTM’s data bank (Banikowski, 1999).
Initially, my lesson plan had limited opportunities for students to create elaborative pathways that that would enable them to access the information. With the revisions, however, metacognitive strategies are now involved, such as self-evaluation on the Marble Slides activity and art project, and requiring students to submit their notes that fully maximize the Cornell Note- taking strategy. As well, the addition of the Algebra Bootcamp, Whiteboard Activity, and Marble Slides will simultaneously impact both episodic and semantic declarative memories.
With the Algebra Bootcamp, students work together to solidify their skills prior to the new information being presented. Since the students will have been utilizing algebra since Grade 8, this activity enables them to transfer these skills into the LTM, should it not already be there. The Whiteboard Activity will review a skill from the previous unit, and the Desmos Marble Slides will rehearse information from the previous day. The Kahoot will review the week’s material in a highly entertaining fashion. The art project will cumulate all skills into one finale in which students will be expected to produce a minimum of 75 equations, that are individually restricted in their domains and ranges. Without question, there now are multiple rehearsal opportunities!
Conclusion
For many people, a mathematics class represents a time plagued with frustration and stress. It is my hope that by incorporating more social, collaborative, brain-based methodologies into my practice, the negative feelings that many students harbor will at least be minimized. Nevertheless, I am not prepared to abandon direct instruction techniques for most of my lessons, as I feel that upper-level math and science demand precision and proper technique. As well, educators should be ever mindful of a significant limitation that cannot be ignored: class time is finite. Creating activities that overlap theories is an ideal way to circumnavigate the time limitation. With time management also being a consideration, a blend of traditional and modern learning approaches is what my view of “21st Century” learning ultimately looks like in my classroom.
References
Banikowski, A. K. (1999). Strategies to enhance memory based on brain-research. Focus on Exceptional Children, 32(2), 1.
Conlan, P., Gallant, M. & Kim, D. (2016). Information Processing Theory Presentation. Retrieved from http://iptheoryetec512.weebly.com/information-processing.html
Glassman, M. (1994). All things being equal: The two roads of Piaget and Vygotsky. Developmental Review, 14(2), 186-214. doi:10.1006/drev.1994.1008
John-Steiner, V., & Mahn, H. (1996). Sociocultural approaches to learning and development: A Vygotskian framework. Educational Psychologist, 31(3), 191.doi:10.1207/s15326985ep3103&4_4
Lutz, S., & Huitt, W. (2003). Information processing and memory: Theory and applications. Educational Psychology Interactive. Retrieved from http://www.edpsycinteractive.org/papers/infoproc.pdf
Orey, M. (2001). Information Processing. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved from http://epltt.coe.uga.edu/
Pass, S. J. (1999). Jean Piaget and Lev Vygotsky: A historical comparison of their early biographies (Doctoral dissertation). Retrieved from http://ezproxy.library.ubc.ca/login?url=http://search.proquest.com.ezproxy.library.ubc.ca/docview/304529396?accountid=14656
Willis, J. (2011). A neurologist makes the case for the video game model as a learning tool. Retrieved from https://www.edutopia.org/blog/neurologist-makes-case-video-game-model-learning-tool
Zamarian, L., Ischebeck, A., & Delazer, M. (2009). Neuroscience of learning arithmetic: Evidence from brain imaging studies. Neuroscience and Biobehavioral Reviews, 33(6), 909-925. doi:10.1016/j.neubiorev.2009.03.005