Abstract
The research shows that the lack of instructional scaffolding and high degree of user control inherent to most HLEs make them difficult learning environments for learners who lack the ability to appropriately self-regulate their learning. Therefore, developers of HLEs must construct these environments in ways that not only promote knowledge acquisition, but also foster and scaffold SRL skills. This chapter introduces a two-tiered (i.e., the micro- and macro- level) approach to analyzing SRL data derived from think aloud protocols, which can be informative in terms of the domain-, task-specific self-regulatory processes that should be scaffolded in particular HLEs. The two-tiered approach provides a bridge between the SRL data and theory by showing how the micro-level learning processes (e.g., judgments of learning) can be used to indicate the degree to which individuals engage in the macro-level categories of self-regulation discussed in SRL models. Findings from a number of our research studies illustrate how analyzing data at both tiers results in a comprehensive understanding of how learners self-regulate in HLEs, and how the nature and quality of that self-regulation interacts with internal and external conditions.
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Greene, J.A., Dellinger, K.R., Tüysüzoğlu, B.B., Costa, LJ. (2013). A Two-Tiered Approach to Analyzing Self-Regulated Learning Data to Inform the Design of Hypermedia Learning Environments. In: Azevedo, R., Aleven, V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5546-3_8
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