Developing high quality learning contents (LCs) and improving their interactions in online learning with e-learning contents is important for greater cognitive abilities of a learner.
Studies on the interactions in online learning have increased and focus has been placed on its increased effectiveness. However, there has been less discussion on the relationship of LC design to learner’s cognition. In addition, there are few qualitative data on the interactions between a learner and LC, and less has been known about the effectiveness of self-feedback in learning processes on increasing the cognitive abilities of a learner. On the other hand, non-structural interaction brings about negative results to learners and cannot improve learner’s cognition, which means less cost-effective. This clearly shows that the interactions with LCs should be improved for successful online learning and, to this end, LCs with a self-feedback structure be designed to suit the intrinsic features of a learner.
Kim Jang Hak, a section head at the Faculty of Distance Education, has designed LCs with new self-feedback structure by considering the personalized characteristics of a learner and described a method for improving learners’ cognition abilities through improved interactions between a learner and LCs.
Based on the previous research findings on feedback and interaction, he divided the learning object into knowledge unit learning nodes and managed the learning control and appropriate interactions through self-feedback. Then, he conducted an experiment to investigate what improvement was made in the learner’s cognition by putting the proposed method into practical application.
The result showed that the self-feedback is a significant factor that gives positive influence on the learner’s cognition.
You can find the details in his paper “Improving Learners' Cognition through Designing Learning Contents with Self-feedback Structure and Advanced Interaction in Online Learning” in “Informatica” (SCI).
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