Tracing self-regulated learning in responsive open learning environments

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Abstract

Self-Regulated Learning (SRL) and related meta-cognitive learning competences help to increase learning progress. However, facilitating the acquisition of such competences with learning technologies is challenging. Training requires an individualized approach and the right balance between the learner’s freedom and guidance. To support SRL, we applied personalisation and adaptive technologies in the development of an Open Source toolkit for Responsive Open Learning Environments (ROLE). In this paper we present a conceptual foundation for the operationalization of self-regulated learning in personal learning environments as a cyclic process model. Furthermore, we present results of a long-term usage data analysis of the ROLE Sandbox, an open and free Web-based hosting environment for personal learning environments. In particular, we trace self-regulated learning activities in three years of productive operation. We conclude our findings with guidelines for self-regulated learning in personal learning environments.

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Renzel, D., Klamma, R., Kravcik, M., & Nussbaumer, A. (2015). Tracing self-regulated learning in responsive open learning environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9412, pp. 155–164). Springer Verlag. https://doi.org/10.1007/978-3-319-25515-6_14

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