Guest post by Ilham Akhundov, Associate Professor Teaching Stream (ilham.akhundov@uwaterloo.ca)
Recently, I attended the Math Teaching Colloquium at University of 蓝莓视频. The speaker Dan Wolczuk delivered a compelling presentation on how cognitive load theory can revolutionize teaching and learning math. The talk highlighted how managing cognitive load effectively can lead to better student engagement and deeper learning. Inspired by this insightful presentation, I decided to dive deeper into the subject.
I quickly realized that there is a significant opportunity to help educators apply cognitive load principles in flipped classrooms and applying cognitive load theory in flipped classrooms can be incredibly effective. Motivated by this insight, I decided to take on the challenge of presenting this topic at the as a keynote speaker.
In flipped classroom, students learn material ahead of time at their own pace and apply their knowledge in class, as opposed to working through their homework primarily afterwards. Pre-class material design and active learning during class can address and manage the difficulty of the material, reduce unnecessary distractions or complex formatting in materials and help students build schemas to connect new information with what they already know.
The essence of flipped learning is to take advantage of the best aspects of in-person and online learning: support from experienced instructors and the ability to learn at your own pace. In a 2017 study done in T眉rkiye, 80 of 160 engineering students were introduced to flipped learning to measure its impact on cognitive load. After eight weeks, the study showed that 鈥渢he cognitive load in the group in which the flipped learning method was used was found to be lower than that of the traditional face-to-face training鈥 ().
Using a tested nine-point scale for cognitive load measurements where 1 means 'very low cognitive load'聽and 9 means 'very high cognitive load', the authors quantified the cognitive load and presented the results in a table:
Cognitive load average scores
| Department | Experimental Group Average Score | Control Group Average Score | Difference |
|
Mechanical Engineering |
4.23 | 6.18 | 1.95 |
The t-test conducted in the study revealed a statistically significant difference in the mean cognitive load scores between the experimental (flipped classroom) and control (traditional instruction) groups. This finding supports the idea that providing students with the opportunity to build a foundational understanding before class results in less intrinsic cognitive load during valuable instructional time. Even though intrinsic cognitive load comes from the difficulty of the material itself, it can be managed by designing lessons that connect to what students already know and by using class time to build on what they鈥檝e learned before class.
However, since the research presented on the effect of flipped learning and incorporation of various senses are done on a similar set of students who likely have closely related learning styles, further research is required in this field to assess the different effects of the proposed instructional
methods on various kinds of learners, including neurodivergent students. Specifically, conducting similar experiments on groups with various kinds of cognitive processing could further establish this field of study as more inclusive.
The effect of cognitive load theory on education is highly relevant in today鈥檚 society as the science behind learning is a growing topic that more psychologists are showing interest in. Furthermore, this theory can help students around the world better realize their learning potential since education is a universal concept. In conclusion, whether you are a curious student or a dedicated educator, cognitive load theory provides valuable insights in working toward maximizing and improving future learning outcomes.
