MVR-CLS: An Automated Approach for Effective Classification of Microlearning Video Resources

Abstract

In the big-data era, massive Open Educational Resources (OERs) can be obtained from the Internet regardless of location or time constraints. Researchers have discussed microlearning as a service to improve learning effectiveness. However, the emergence of OERs leads to the challenge of searching for appropriate and relevant microlearning resources. In this paper, an automated video classification approach named “MVR-CLS” is proposed to organize and classify microlearning resources, so that the learners can browse for learning resources in a manageable way. Speech-To-Text data mining technique is applied to transcribe a learning video and to further analyze the video content. A 3-tier learning category structure is proposed to organize a collection of microlearning videos into appropriate learning categories. “MVR-CLS” has shown the capability to classify the microlearning videos into a finer-grained learning category as compared to the existing work. To evaluate the accuracy of the proposed approach, the classification result is validated against to the metadata of the OERs. The classification result can promote better fit of learners’ interests for content recommendations and thus enhancing the recommendation accuracy in future work.