An adaptive Metalearner-based flow: a tool for reducing anxiety and increasing self-regulation

Abstract

Anxiety and self-regulation are the most common problems among the college student population. There are few attempts found in the literature to promote the development of students’ cognitive and metacognitive abilities in online learning environments. In addition, mechanisms for overcoming or reducing individuals’ anxiety in a computer-mediated environment is yet to be fully characterized. This study was conducted to investigate the potential of integrating the concept of flow into the design of a Metalearner (MTL) to help reduce anxiety and increase self-regulation among students. The design of MTL was based on the development of adaptive strategies to balance between the challenge of the task and user skills. A total of 260 participants were asked to use the system and respond to an online questionnaire that asked about flow antecedents, experience, and consequences. The structural model results showed that incorporating flow into the design of MTL can help reduce anxiety and improve self-regulation among students. Our findings can be used to enrich students’ online learning experience and inform designers and developers of learning systems about the importance of regulating task complexity according to the challenge/skills balance. This would help learners to process the presented information meaningfully and to make the inferences necessary for understanding the learning content.