Article Review 1 – Lori Sowa
While researching articles related to active learning strategies for online courses, I came across an article on personalized learning environments for students taking online remedial math courses. This article captured my interest because I am teaching a remedial math course for the first time this semester. I know the content of the course well, but helping students to surmount significant challenges to learning the material, working through math anxiety, and being engaged when many don’t see a direct connection to their educational goal is a challenge.
In The Role of Affective and Motivational Factors in Designing Personalized Learning Environments, Kim (2012) outlines guidelines for developing Virtual Change Agents (VCAs) to accommodate students’ emotional and motivational needs specific to online remedial math courses. VCAs are described as human-like animations that are designed to facilitate positive changes in learners’ attitudes and are personalized to meet the needs of each individual student. The VCAs can provide personalized motivation by allowing the student to choose the subject matter of example problems (e.g. motorcycle repair, dieting calculations, etc.) — thus improving student perceptions of task value and controllability, which ultimately can lead to students’ positive reappraisal of their situation. The author also suggests that student interactions with VCAs can be designed to promote emotional regulation skills, and provides a framework on how to accomplish this based upon prior research in affective and motivational factors.
This is the first I’ve heard of VCAs, and to be honest my first reaction is doubt about whether learners will relate to an animated human providing pre-programmed advice. The effectiveness of this specific strategy remains to be seen — the author acknowledges the theories and guidance described in the paper have yet to be validated, and is actively seeking researchers to perform these studies. But the author does point to a number of studies related to “Computers Are Social Actors’ (CASA) theory that support the idea that users relate to computers as they do to people. The study of computer-person interactions is fascinating to me, and is certainly an area that is quickly evolving and expanding. It is unclear, however, if any of the studies referenced support a sustained relationship through purely computerized interaction that leads to a substantial change in emotional and motivational state for populations relevant to this study. I can certainly envision the possibility, though, and in fact it is a bit mind-boggling to think about the potential applications.
The underlying strategy used in creating the personalized learning environment is to provide support to overcome both emotional and motivational barriers to learning math content. The author quotes a relevant analogy from Buck (1985) who states that ‘‘just as energy is a potential that manifests itself in matter, motivation is a potential that manifests itself in emotion. Thus motivation and emotion are seen to be two sides of the same coin, two aspects of the same process’’. The strategies outlined to work through these barriers would apply to any classroom, whether online or face-to-face. However, the online classroom may allow for greater personalization when instructor time is limited. For example, I try to choose relevant example problems in my lecture based upon what I know about my students’ educational goals and interests. But I will never be able to do this for each student. Allowing students to have control over the context of the problems aligns with the constructivist theory of learning, while fostering motivation is an essential tenant of cognitive psychology.
The research addresses an important need — as the author reports, 1 in 3 students entering the University are placed into a remedial math course. These students bring a wealth of issues that require personalized scaffolding to address and promote student success. To intervene and provide that personalized support in an online environment, instructors and instructional designers have to find ways of predicting when and why students will disengage, and provide built-in solutions to try to prevent this. It will be interesting to see how effective VCAs can be in providing this support system.
Buck, R. (1985). An integrated view of motivation and emotion. Psychological Review, 92(3), 389—413.
Kim, C. (2012). The Role of Affective and Motivational Factors in Designing Personalized Learning Environments. Educational Technology Research and Development 60(4), 563—584.