Utilizing Neuroscience-Based Learning Principles in the Higher Education Classroom
Barbara Nadeau, Quinnipiac University
Abstract: Neuroscience has begun to prove what many college professors have long suspected -cramming for exams doesn't work, multitasking is inefficient, and completing the readings before
class is helpful. This practice session will explain the current research regarding how the brain
learns and the factors that influence learning. Participants will be provided with examples of how
to incorporate these principles into their classrooms and will be invited to reflect upon and discuss
their own use of these learning principles.
Literature Review
In the past fifteen years neuroscientists have developed a greater understanding of the way the brain learns and the
factors that influence learning (Doyle & Zakrajsek, 2013). It is now understood that learning is a process of building
new neural networks and that there are certain learning strategies that can facilitate the development of these new
networks (McGinty, Radin, & Kaminski, 2013). Therefore we can now design our courses to take advantage of the
revelations from neuroscience. For instance, it has been demonstrated that memory for new information is stronger
when studying is spaced over a period of a week rather than all in one day (Jang, Wixted, Pecher, Zeelenberg, &
Huber, 2012; Pavlik & Anderson, 2008). Researchers have also found that some of the strategies that are regularly
used by students actually inhibit learning. For example multi-tasking is inefficient and impedes learning (Ravizza,
Hambrick, & Fenn, 2014) and even the longstanding practice of using flashcards for learning has been shown to be
ineffective for retaining detailed information (Reagh & Yassa, 2014). On the other hand, strategies such as the use
of concept maps and scaffolded learning are being supported by the neuroscience research. Researchers have
shown that it is necessary to fully engage the lateral pre-frontal cortex in learning tasks in order to later be able to
generalize the learning to new situations and that the lateral pre-frontal cortex becomes engaged when new learning
is linked with prior learning (Cole, Laurent, & Stocco, 2013). Developing an understanding of these neuroscience
concepts can improve teaching and learning (Doyle & Zakrajsek, 2013).
Objectives for the Practice Session
Upon completion of this session, participants will be able to:
1.
Identify the primary brain areas implicated in learning
2.
Explain, in general terms, what is occurring in the brain during learning
3.
Develop classroom strategies that incorporate the neuroscience of learning.
Description of the Practice
This session will begin with a brief, general, interactive neuroanatomy primer to orient participants to the brain areas
implicated in learning. Major findings relative to memory, the impact of elaboration and distributed practice on
learning, novelty and mindset will be discussed. The research regarding each concept will be described followed by
examples of how these concepts can be incorporated into the classroom. Participants will be invited throughout the
presentation to think about which of these concepts they are already utilizing and to share their strategies. Research
regarding additional factors that influence learning such as sleep and exercise will also briefly be presented.
Discussion
Many higher education teachers have already instinctively discovered these principles but science is now providing
credence to these practices. I have begun sharing this brain science with my students in order to promote these
active learning practices. I have also adjusted my teaching style to incorporate these principles. Similar to others
(Stupans, Scutter, & Pearce, 2010), since making these changes I have noticed an improvement in student grades as
well as on my teaching evaluations.
References
Cole, M. W., Laurent, P., & Stocco, A. (2013). Rapid instructed task learning: a new window into the human brain’s
unique capacity for flexible cognitive control. Cognitive, Affective & Behavioral Neuroscience, 13(1), 1–22.
doi:10.3758/s13415-012-0125-7
Doyle, T., & Zakrajsek, T. (2013). The new science of learning. Sterling: Stylus Publishing LLC.
Jang, Y., Wixted, J. T., Pecher, D., Zeelenberg, R., & Huber, D. E. (2012). Decomposing the interaction between
retention interval and study/test practice: the role of retrievability. Quarterly Journal of Experimental
Psychology (2006), 65(5), 962–75. doi:10.1080/17470218.2011.638079
McGinty, J., Radin, J., & Kaminski, K. (2013). Brain-Friendly Teaching Supports Learning Transfer. New
Directions for Adult and Continuing Education, 2013(137), 49–59. doi:10.1002/ace.20044
Pavlik, P. I., & Anderson, J. R. (2008). Using a model to compute the optimal schedule of practice. Journal of
Experimental Psychology. Applied, 14(2), 101–17. doi:10.1037/1076-898X.14.2.101
Ravizza, S. M., Hambrick, D. Z., & Fenn, K. M. (2014). Non-academic internet use in the classroom is negatively
related to classroom learning regardless of intellectual ability. Computers & Education, 78, 109–114.
Reagh, Z. M., & Yassa, M. A. (2014). Repetition strengthens target recognition but impairs similar lure
discrimination: Evidence for trace competition. Learning and Memory, 21(7). doi:342 DOI:
10.1101/lm.034546.114
Stupans, I., Scutter, S., & Pearce, K. (2010). Facilitating Student Learning: Engagement in Novel Learning
Opportunities. Innovative Higher Education, 35(5), 359–366. doi:http://dx.doi.org/10.1007/s10755-010-91486