Cognitive Engagement: a More Reliable Proxy for Learning?

Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning

Reinforcement learning (RL) methods employing powerful function approximators like neural networks have become an interesting approach for optimal control. Since they learn a policy from observations, they are also applicable when no analytical description of the system is available. Although impressive results have been reported, their handling in practice is still hard, as they can fail at re...

A Framework of E-learning System for Students’ Engagement with Cognitive Tasks to Enhance Learning Performance

The use of integrating technology with learning has been a pivot that transformed the role of instructors and students; and makes it both technology-driven and learner-centric control-mediated. It has been a research-based issue in connecting technology and learning theories for effective learning performance. Learning should be a platform where students are actively engaged in a process to con...

Proxy versus real-time patient engagement.

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Cognitive engagement in the problem-based learning classroom

The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated. Moreover, it was examined how cognitive engagement develops as a function of the learning process and the extent to which cognitive engagement determines subsequent...

The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes