Coming into the field of Learning Sciences with a cognitive psychology background provided me with a solid foundational understanding of psychological mechanisms behind information processing and human memory. However, throughout the course of seminar, I have gained an appreciation for several other factors involved in the process of learning; namely, motivation, emotion, and regulatory strategies. I also gained an appreciation for interdisciplinary research, namely in the fields of medicine and engineering.
Meredith Young challenged the applicability of the typical definition of expertise in the field of medicine. She introduced an intriguing paradox in her presentation: medical experts show increased accuracy and regulation in medical diagnostic tasks but a reduced retention of new biomedical knowledge. What makes this observation so intriguing is that most medical experts are almost by definition more senior physicians. Through her research, she attempts to characterize decision-making in the healthy-aging population, thereby helping to tease apart the aging component and the expertise component of medical expertise.
The moment her argument really clicked with me was when she used an analogy from cognitive psychology to describe the cognitive process behind decision-making in healthy-aging individuals. When describing the early hypothesis confirmation bias, she used the terms primacy effect and working memory, which were concepts familiar to me. That is, in a contingency paradigm judgment task, seniors, compared to adults, tend to rely too heavily on earlier-presented information and too often neglect to change their already-formed hypotheses even after encountering conflicting information. This, to me, is very analogous to the primacy effect found in traditional memory experiments. Dr. Young posed an explanation for the seniors' confirmation bias phenomenon stating that it is because of their age-related declines in working memory capacity that their ability to integrate new, conflicting information with the information already being held in the working memory is hampered.
All in all, I appreciated hearing from a speaker who not only studies in a specialized discipline but also makes strong ties with cognitive psychology. It helped me realize the possibilities of merging academic and applied research and that, in turn, made me comfortable about my role as a science-oriented learning sciences researcher with a background in cognitive psychology.
As someone who has taken an undergraduate course in Human Factors, I was excited to hear about some of the human-computer interaction (HCI) research happening at the Centre for Intelligent Machines. Adriana Olmos's research showed me how HCI research can cross paths with learning sciences research. After her presentation, I was inspired to look into further possibilities of learning sciences research at the medical simulation centre. I was driven to do so particularly because I noticed that there was a lack of focus on the learning process and on learning outcomes. The reason why such research is missing from the engineering field may have something to do with the scientists' obligations to meeting the needs of the engineers. I have noticed this and other such issues in the challenges of initiating inter-disciplinary collaboration.
NON-COGNITIVE ASPECTS OF LEARNING SCIENCES
My concept map represents some of the things I've learned from Drs. Nathan Hall, Reinhard Pekrun, and Roger Azevedo. I identified "cognitive resources" as one of the mechanisms mediating learning and performance, in addition to motivation, emotions, and regulation, because I wanted to point out that, before this seminar, "cognitive resources" was the only aspect of learning sciences that I had an appreciation for because of my background in cognitive psychology, where I focused heavily on information processing theories.
Under motivation, I first included the classic notion of reward: intrinsic and extrinsic rewards because that's something I was already familiar with. Based on Nathan Hall's topic of attribution theory, I showed that that motivation is affected by one's attribution style; specifically, the way one attributes failure or poor performance to a certain cause (for example, lack of ability or lack of effort). Thus, attributional style affects motivation and is classified based on factors of internability, controllability, and stability. A poor attributional style can lead to poor performance, so attributional retraining can be used as an intervention to make small but significant modifications to a student's attributional style. Reading the description of the procedure made me think about the connection between attributional retraining and cognitive-behavioural therapy; both involve the modification of dysfunctional or maladaptive thought patterns. From Nathan Hall's research, I found out that even a simplistic intervention can make a big difference in motivation and achievement because, in many cases, all a student needs is a nudge in the right direction. It was furthermore interesting to find out how different types of learners (i.e., low elaborators, high elaborators, and those with high self-confidence) react differently to attributional retraining and therefore need an adapted version of the intervention. In other words, it is important to take into consideration individual differences when devising ways to influence a learner's motivation (Hall et al., 2007).
These were important lessons for me to learn because it was the first time I realized what an important part affect and motivation plays in learning and how these effects can be scientifically studied and intervened. After Nathan Hall's presentation, I felt prepared to dive into Reinhard Pekrun's topics.
According to Reinhard Pekrun, a learner's emotions affect just about everything they do. That's why I put arrows between achievement emotions and motivation, regulation, and cognitive resources. In fact, if I were to investigate further, I would probably find that these arrows should be bidirectional and greater in quantity, representing the complex, reciprocal nature among the factors. For example, achievement emotions, whether positive or negative, will take up some of a learner's cognitive resources. However, Dr. Pekrun also stated that certain types of emotions can help a student focus on a learning task and, thus, it's worth the extra consumption of resources.
According to the Control Value Theory of Achievement Emotions, the effects of emotions on achievement are mediated by cognitive appraisals. The two types of cognitive appraisals are subjective value, which can be intrinsic or extrinsic, and subjective control, which can be regarding expectancies or attributions. Because of the complex relationship among these topics, they were difficult for me to grasp at first. What really helped me was sharing my "positive classroom experience" with the class via WebCT, where I described and generated theory-driven explanations for why I enjoyed my favourite undergraduate course, Cognitive Development.
The topic of self-regulated and externally-regulated learning was most relevant to my master's thesis, as I am working with Roger Azevedo's MetaTutor project. Rather than describing the new things I learned from the presentation and readings, I will describe how some of the other topics have contributed to my grasping of a more holistic, comprehensive view on adaptive hypermedia environments and intelligent tutoring systems.
Nathan Hall's presentation made me question the sources of motivation and the style of causal attribution of the users of MetaTutor. For one, I would be interested in knowing whether or not each user has a personal interest in the topic of the circulatory system. If not, perhaps it would be beneficial for the agents to help provide (or prompt for the user to generate) a context that is meaningful to the learner. More in line with Nathan Hall's research, it could prove to be beneficial to provide a condensed, electronic version of his attributional retraining intervention at the start of the learning session to see whether or not that would have an effect on the learners' motivation and, in effect, their achievement of the learning goals.
Learning about achievement emotions from Reinhard Pekrun made me realize how important it is to try to understand the learners' emotion while they are engaged with MetaTutor. As Pekrun outlined, there is a large variety of achievement emotions, all having multiple antecedents and consequences. By understanding their emotions, we can more accurately dissect and understand a learner's self-regulatory behaviours. Furthermore, the factor of emotion can potentially be an important component of the agents. According to Dr. Pekrun, it is helpful when a student's teacher provides genuine emotional responses to the student's learning progress or failures. My immediate thought was that the MetaTutor agents should display such emotions; it would be interesting to see whether or not these emotions, reciprocally, affect the students' achievement emotions and, in turn, help them adapt to the external regulation of learning.
The Research Seminar in Learning Sciences provided me with a comprehensive overview of topics in the discipline, all of which have led to broader perspectives of the definition of learning sciences. In particular, the topics of engineering and medicine have led me to gain an appreciation for the interdisciplinary nature of the field. The topics of motivation, emotion and regulatory strategies have led me to gain an appreciation for the non-cognitive aspects of the learning process. Furthermore, I have had the opportunity to integrate my new knowledge, generate original ideas, and make connections to my master's and doctoral theses.