A rapid auto-indexing technology for designing readable e-learning content

  • Pao-Ta Yu National Chung Cheng University
  • Yuan-Hsun Liao National Chung Cheng University
  • Ming-Hsiang Su National Chung Cheng University
  • Po-Jen Cheng National Chung Cheng University
  • Chun-Hsuan Pai National Chung Cheng University
Keywords: Scene detection, instructional video, anchored or access point, mastery learning, regulated learning

Abstract

A rapid scene indexing method is proposed to improve retrieval performance for students accessing instructional videos. This indexing method is applied to anchor suitable indices to the instructional video so that students can obtain several small lesson units to gain learning mastery. The method also regulates online course progress. These anchored points not only provide students with fast access to specific material but also can link to certain quizzes or problems to show the interactive e-learning content that course developers deposited in the learning management system, which enhances the learning process. This allows students to click on the anchored point to repeat their lesson, or work through the quizzes or problems until they reach formative assessment. Hence, their learning can be guided by the formative assessment results.

Author Biographies

Pao-Ta Yu, National Chung Cheng University
Dept. of Computer Science and Information Engineering
Yuan-Hsun Liao, National Chung Cheng University
Dept. of Computer Science and Information Engineering
Ming-Hsiang Su, National Chung Cheng University
Dept. of Computer Science and Information Engineering
Po-Jen Cheng, National Chung Cheng University
Dept. of Computer Science and Information Engineering
Chun-Hsuan Pai, National Chung Cheng University
Dept. of Computer Science and Information Engineering
Published
2012-11-01
How to Cite
Yu, P.-T., Liao, Y.-H., Su, M.-H., Cheng, P.-J., & Pai, C.-H. (2012). A rapid auto-indexing technology for designing readable e-learning content. The International Review of Research in Open and Distributed Learning, 13(5), 20-38. https://doi.org/10.19173/irrodl.v13i5.1246