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The Journal of Supercomputing

, Volume 75, Issue 4, pp 1971–1989 | Cite as

Activity index model for self-regulated learning with learning analysis in a TEL environment

  • Kyungrog Kim
  • Nammee MoonEmail author
Article
  • 79 Downloads

Abstract

Various learner-oriented teaching–learning models are spreading along with development of the technology-enhanced learning (TEL) environment and the spread of the massive open online course (MOOC). Vast amounts of various data are being created and accumulated from learning activities based on the TEL environment. Also, a self-regulated learning ability is required in the MOOC environment because the learning process is constituted on students making decisions by themselves. Accordingly, this study is aimed at suggesting an activity index model based on self-regulated learning and an activity index based on self-regulated learning. It is intended to provide a means to collect proof of what influences the teaching–learning activity. This model is intended to set a learning activity standard on the basis of general activity, interaction activity, and achievement activity by students. It will be possible to analyze the student’s participation level based on the activity index, which is based on self-regulated learning, to induce participation in the teaching–learning activity, and to recommend more appropriate learning activity elements. The student data are divided into score-related, time-related, and count-related groups for applications. The stabilization of the data was confirmed through time series analysis. In multiple regression analysis, the academic achievement element was set by the target variable, and the relationships among explanatory variables were confirmed. It was understood from the explanatory variables that similar student groups were highly concerned with notice participation in the learning activity. It will be possible to analyze the students’ participation levels, induce participation in the teaching–learning activities, and recommend more appropriate learning activity elements on the basis of an activity index based on self-regulated learning.

Keywords

Self-regulated learning Learning activity index Learning analysis Activity index model Time series data 

Notes

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 2016015499). This work was also supported by Research Projects for Senior Researchers through the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF) (No. 2017-2017008886).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringHoseo UniversityAsanKorea
  2. 2.Department of Computer SoftwareHoseo UniversityAsanKorea

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