Collection

Metacognition and self-directed learning: Skills for self-assessment in technology-based learning

Submission status
Closed
The scope of the special issue is to bring together theoretical models and empirical research about metacognitive skills and self-directed learning skills necessary for successful self-assessment in technology-based learning. There is a broad range of possible applications of self-assessment in technology-based learning. It is well suited for adaptive tasks (e.g., Benchoff et al., 2018) using learning analytics and artificial intelligence, in which, for example, the current knowledge or the difficulty of questions play a role.

Metacognition is knowledge about one's own knowledge (e.g., Flavell, 1979), including the monitoring and control of one's own cognitive processes. Self-directed learning “is an intentional learning process that is created and evaluated by the learner” (ISSDL, 2020). Metacognitive and self-directed learning skills are fundamental for successful self-assessment. Self-assessment is mainly used in formative tests and tasks, e.g., to let students see where they have gaps in their knowledge or, more generally, areas that still need to be improved (e.g., Seifried, & Spinath, 2021; Panadero et al., 2019). A critical point in self-assessment is whether students have the necessary skills.

Call for Papers

Editors

  • Prof. Dr. Egon Werlen Prof. Dr. Egon Werlen  &

    Prof. Dr. Egon Werlen

    Egon Werlen works at the Institute for Research in Open, Distance and eLearning and the UNESCO Chair on Personalised and Adaptive Distance Education at Swiss Distance University of Applied Sciences. He is extraordinary associated professor of the Research Unit Self-Directed Learning at the North-West University in South Africa. He is teaching methods and statistics, psychological aspects of learning, and health psychology. His research concerns adaptive learning, emotions and learning, self-directed learning and self-assessment of answers to open-ended questions often involving machine learning and AI. egon.werlen@ffhs.ch

  • Prof. Dr. Dorothy Laubscher Prof. Dr. Dorothy Laubscher

    Prof. Dr. Dorothy Laubscher

    Dorothy Laubscher is associate professor of Mathematics Education in the Faculty of Education, member of the Research Unit Self-Directed Learning, and chairholder of the UNESCO Chair on Multimodal Learning and Open Educational Resources at the North-West Universityin South Africa. She has taught modules in Mathematics Education and supervises post-graduate students. Her research includes Mathematics Education, technology-enhanced learning, self-directed learning (SDL), open educational resources, and blended and multimodal learning to foster SDL. Her current projects explore multimodal learning to promote SDL. dorothy.laubscher@nwu.ac.za

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