A Coding Mechanism for Analysis of SRL Processes in an Open-Ended Learning Environment

Abstract

Open-Ended Learning Environments (OELE) support learning conceptually rich domains. However, widespread use of such OELE has posed several challenges for novice learners, for example, decision making tasks such as trade-off analysis, negotiation, etc. Since the nature of OELE is non-linear and open-ended, it requires the need of employing several self- regulatory processes such as planning, cognitive strategies, metacognitive monitoring, etc. To analyse these self-regulated learning (SRL) processes in an OELE, we introduce and discuss a coding mechanism based on Pintrich’s framework of SRL and the design of a learning environment. The mechanism discusses several cognitive and metacognitive processes and observable indicators that can be representative/suggestive of a specific regulatory process that a learner might be displaying. To test the mechanism, a retrospective think-aloud (N=10) was conducted. Our primary contribution is developing and implementing the proposed coding mechanism. The findings of the work presented in the paper indicate a detailed understanding of the regulatory processes employed by learners while solving an open-ended problem in an OELE.