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


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.


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



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).


  1. 1.
    Siemens G (2013) Learning analytics: the emergence of a discipline. Am Behav Sci 57(10):1380–1400CrossRefGoogle Scholar
  2. 2.
    Khalil M, Ebner M (2016) When learning analytics meets MOOCs-a review on iMooX case studies. In: International Conference on Innovations for Community Services. Springer, ChamGoogle Scholar
  3. 3.
    Scheffel M, Drachsler H, Stoyanov S, Specht M (2014) Quality indicators for learning analytics. J Educ Technol Soc 17(4):117Google Scholar
  4. 4.
    Jo I, Kim J (2013) Investigation of statistically significant period for achievement prediction model in e-learning. J Educ Technol 29(2):285–306MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hu YH, Lo CL, Shih SP (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav 36:469–478CrossRefGoogle Scholar
  6. 6.
    Chatti MA, Muslim A, Schroeder U (2017) Toward an open learning analytics ecosystem. In: Kei Daniel B (ed) Big data and learning analytics in higher education. Springer, ChamGoogle Scholar
  7. 7.
    Kim K (2016) Learner activity modeling based on teaching and learning activities data. KIPS Trans Softw Data Eng 5(9):411–418CrossRefGoogle Scholar
  8. 8.
    Mining TED (2012) Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. In: Proceedings of Conference on Advanced Technology for EducationGoogle Scholar
  9. 9.
    Ren Z, Rangwala H, Johri A (2016) Predicting performance on MOOC assessments using multi-regression models. arXiv:1605.02269
  10. 10.
    Manzo M (2017) A model for users behavior analysis and forecasting in Moodle. J e-Learn Knowl Soc 13(2):129–139Google Scholar
  11. 11.
    Zhang JH, Zou Q (2016) Group learning analysis and individual learning diagnosis from the perspective of Big Data. In: 2016 IEEE International Conference Cloud Computing and Big Data Analysis (ICCCBDA). IEEEGoogle Scholar
  12. 12.
    Zhang W, Huang X, Wang S, Shu J, Liu H, Chen H (2017) Student performance prediction via online learning behavior analytics. In: 2017 International Symposium Educational Technology (ISET). IEEEGoogle Scholar
  13. 13.
    Kuo YC, Walker AE, Schroder KE, Belland BR (2014) Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet High Educ 20:35–50CrossRefGoogle Scholar
  14. 14.
    Pardo A, Han F, Ellis RA (2017) Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Trans Learn Technol 10(1):82–92CrossRefGoogle Scholar
  15. 15.
    Li S, Yu C, Hu J, Zhong Y (2016) Exploring the effect of behavioral engagement on learning achievement in online learning environment: learning analytics of non-degree online learning data. In: 2016 International Conference Educational Innovation through Technology (EITT). IEEEGoogle Scholar
  16. 16.
    Ndubisi NO (2004) Factors influencing e-learning adoption intention: examining the determinant structure of the decomposed theory of planned behavior constructs. In: Proceedings of the 27th Annual Conference of HERDSA. pp 252–262Google Scholar
  17. 17.
    Zhou M (2016) Chinese university students’ acceptance of MOOCs: a self-determination perspective. Comput Educ 92:194–203CrossRefGoogle Scholar
  18. 18.
    Chu TH, Chen YY (2016) With Good We Become Good: understanding e-learning adoption by theory of planned behavior and group influences. Comput Educ 92:37–52CrossRefGoogle Scholar
  19. 19.
    Pellas N (2014) The influence of computer self-efficacy metacognitive self-regulation and self-esteem on student engagement in online learning programs: evidence from the virtual world of Second Life. Comput Hum Behav 35:157–170CrossRefGoogle Scholar
  20. 20.
    IMS Global Learning Consortium (2013) Learning measurement for analytics whitepaper. Accessed 15 Nov 2017
  21. 21.
    Zhang W, Huang X, Wang S, Shu J, Liu H, Chen H (2017) Student performance prediction via online learning behavior analytics. In: 2017 International Symposium on Educational Technology (ISET). IEEEGoogle Scholar
  22. 22.
    Kovanović V, Gašević D, Hatala M, Siemens G (2017) A novel model of cognitive presence assessment using automated learning analytics methods. Analytics for Learning. Accessed 15 Nov 2017
  23. 23.
    Ruipérez-Valiente JA, Muñoz-Merino PJ, Leony D, Kloos CD (2015) ALAS-KA: a learning analytics extension for better understanding the learning process in the Khan Academy platform. Comput Hum Behav 47:139–148CrossRefGoogle Scholar
  24. 24.
    Kim K, Choi YJ, Kim M, Lee JW, Park DS, Moon N (2015) Teaching–learning activity modeling based on data analysis. Symmetry 7(1):206–219CrossRefGoogle Scholar
  25. 25.
    Goyal Mukta, Yadav Divakar, Tripathi Alka (2017) An intuitionistic fuzzy approach to classify the user based on an assessment of the learner’s knowledge level in e-learning decision-making. J Inf Process Syst 13(1):57–67Google Scholar
  26. 26.
    Aghababaei S, Makrehchi M (2017) Activity-based Twitter sampling for content-based and user-centric prediction models. Hum Centric Comput Inf Sci 7(3):1–20Google Scholar
  27. 27.
    Pedersen T, Johansen C, Jøsang A (2018) Behavioural computer science: an agenda for combining modelling of human and system behaviours. Hum Centric Comput Inf Sci. Google Scholar
  28. 28.
    Liew TW, Zin NAM, Sahari N (2017) Exploring the affective, motivational and cognitive effects of pedagogical agent enthusiasm in a multimedia learning environment. Hum Centric Comput Inf Sci. Google Scholar

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

Personalised recommendations