Browse > Article
http://dx.doi.org/10.9717/kmms.2018.21.11.1353

Restructure Recommendation Framework for Online Learning Content using Student Feedback Analysis  

Choi, Ja-Ryoung (Research Institute of ICT Convergence, Sookmyung Women's University)
Kim, Suin (School of Computing, KAIST)
Lim, Soon-Bum (Dept. of Information Technology Engineering, Sookmyung Women's University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.11, 2018 , pp. 1353-1361 More about this Journal
Abstract
With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.
Keywords
E-learning; Student Feedback; Content Restructuring; Learning Contents;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Coursera. https://www.coursera.org/ (aceessed Aug., 10, 2018).
2 Khan Academy. https://ko.khanacademy.org/ (aceessed Aug., 10, 2018)
3 S. Zyto, D. Karger, M. Ackerman, and S. Mahajan, "Successful Classroom Deployment of a Social Document Annotation System," Proceedings of the Special Interest Group on Computer Human Interaction Conference on Human Factors in Computing Systems, pp. 1883-1892, 2012.
4 E.L. Glassman, J. Kim, A.M. Hernandez, and M.R. Morris, "Mudslide: A Spatially Anchored Census of Student Confusion for Online Lecture Videos," Proceedings of the 33rd Annual Association for Computing Machinery Conference on Human Factors in Computing Systems, pp. 1555-1564, 2015.
5 D.W. Yoon, N. Chen, B. Randles, A. Cheatle, C.E. Lockenhoff, S.J. Jackson, et al., "Rich Review++: Deployment of a Collaborative Multi-modal Annotation System for Instructor Feedback and Peer Discussion," Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, pp. 195-205, 2016.
6 J.R. Choi, J.S. Hwang, E.J. Sin, and S.B. Lim, "A Feedback Clue Model for Dynamically Updating e-book Content from User Feedback," Journal of Korea Multimedia Society, Vol. 20, No. 2, pp. 313-321, 2017.   DOI
7 S.E. Kim and M.G. Park, "Design and Implementation of Customized Learning System for Reusable Learning Objects," Proceedings of the Conference of the Korea Multimedia Society, pp. 311-314, 2006.
8 J.S. Kim, "Design of Evolutionary u-Learning Using Intelligent Agent with Machine Learning," Proceedings of the Conference of the Korea Multimedia Society, pp. 302-306, 2007.
9 Y.W. Lim and H.K. Lim, "Reconstruction of e-Learning Contents based on Web 2.0 and the Level Diagnosis," The Journal of the Korea Contents Association, Vol. 10, No. 7, pp. 429-437, 2010.   DOI
10 Udacity. https://www.udacity.com/ (accessed Aug., 10, 2018).
11 S. Kim, J.W. Kim, J. Park, and A. Oh, "Elivate: A Real-Time Assistant for Students and Lecturers as Part of an Online CS Education Platform," Proceedings of the Third ACM Conference on Learning@ Scale, pp. 337-338, 2016.