Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

  • Authors

    • Nadirah Mohamad
    • Nor Bahiah Ahmad
    • Dayang Norhayati Abang Jawawi
    2018-05-22
    https://doi.org/10.14419/ijet.v7i2.29.13662
  • Massive Open Online Course MOOC, Student Retention, Student’s Online Interaction, Predictive Analytics

  • Abstract

    Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.

     

     

  • References

    2. style='font-size:8.0pt;color:#111111;mso-ansi-language:EN-CA'>
    3. style='mso-element:field-begin'>
    4. style='mso-spacerun:yes'> ADDIN EN.REFLIST
    5. field-separator'>[1] Yousef AMF, Chatti MA, Schroeder U, andHarald Jakobs MW, editors. A Review of the State-of-the-Art. Proceedings of CSEDU2014, 6th International Conference on Computer Supported Education; 2014.

      [2] Ejreaw AM, Drus SM. The challenges of massive open online courses (mooc)–a preliminary review. Proceedings of the 6th International Conference on Computing and Informatics. 2017:7.

      [3] Henek O. MOOC Retention Rate and Motivation. SDU: University of Southern Denmark, Kolding; 2017.

      [4] Gomez-Zermeno MG, De La Garza LA. Research analysis on mooc course dropout and retention rates. Turkish Online Journal of Distance Education. 2016.

      [5] Guo Z, Xiao L, Van Toorn C, Lai Y, Seo C. Promoting online learners’ continuance intention: an integrated flow framework. Information & Management. 2016;53(2):279-95.

      [6] Hew KF. Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology. 2016;47(2):320-41.

      [7] Ghazali NB, Nordin MS. The Perception of University Lecturers of Teaching and Learning In Massive Open Online Courses (MOOCs). Journal of Personalized Learning. 2017;2(1):52-7.

      [8] Tseng S-F, Tsao Y-W, Yu L-C, Chan C-L, Lai KR. Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning. 2016;11(1):8.

      [9] Fadzil M, Latif LA, Azzman TAMTM. MOOCs in Malaysia: a preliminary case study. E-Asem Forum: Renewing The Lifelong Learning Agenda For The Future. 2015.

      [10] Shahiri AM, Husain W. A review on predicting student's performance using data mining techniques. Procedia Computer Science. 2015;72:414-22.

      [11] Gašević D, Dawson S, Rogers T, Gasevic D. Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education. 2016;28:68-84.

      [12] Siegel E. Predictive analytics. Hoboken: Wiley. 2013.

      [13] Brinton CG, Chiang M, editors. Mooc performance prediction via clickstream data and social learning networks. Computer Communications (INFOCOM), 2015 IEEE Conference on; 2015: IEEE.

      [14] Khalil M, Ebner M, editors. Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? EdMedia: World Conference on Educational Media and Technology; 2016: Association for the Advancement of Computing in Education (AACE).

      [15] Yang T-Y, Brinton CG, Joe-Wong C, Chiang M. Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks. IEEE Journal of Selected Topics in Signal Processing. 2017.

      [16] Whitehill J, Mohan K, Seaton D, Rosen Y, Tingley D. Delving Deeper into MOOC Student Dropout Prediction. arXiv preprint arXiv:170206404. 2017.

      [17] Freitas S, Gibson D, Du Plessis C, Halloran P, Williams E, Ambrose M, et al. Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology. 2015;46(6):1175-88.

      [18] Wong BTM, Wong BTM. Learning analytics in higher education: an analysis of case studies. Asian Association of Open Universities Journal. 2017;12(1):21-40.

      [19] Cao G, Duan Y. Understanding Learning Analytics from an IT Affordance Perspective. 2017.

      [20] Sclater N. Developing a Code of Practice for learning analytics. Journal of Learning Analytics. 2016;3(1):16-42.

      [21] Mat U, Buniyamin N, Arsad PM, Kassim R, editors. An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention. Engineering Education (ICEED), 2013 IEEE 5th Conference on; 2013: IEEE.

      [22] Veeramachaneni K, Halawa S, Dernoncourt F, O'Reilly U-M, Taylor C, Do C. Moocdb: Developing standards and systems to support MOOC data science. arXiv preprint arXiv:14062015. 2014.

      [23] Kadir ZA, Baboo SB, Rosni NS, Rahman ZHA, Bakar NA. Design & development of digital learning resource (BMT): blended learning approach. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication; Beppu, Japan. 3022234: ACM; 2017. p. 1-7.

      [24] Bakar Z, Ali R, Zainal Azrie Shuib M. The effect of learning potential enhancement intervention (lpei) on student’s regulation of learning at school2017.

      [25] Tai SS, Ah SHAB. The untold truancy stories: a case study in Malaysia. The Malaysian Journal of Social Administration. 2017;12(1):76-99.

      [26] Gasevic D, Kovanovic V, Joksimovic S, Siemens G. Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning. 2014;15(5).

      [27] Sunar A, White S, Abdullah N, Davis H. How learners’ interactions sustain engagement: a MOOC case study. IEEE Transactions on Learning Technologies. 2016.

      [28] Taylor C, Veeramachaneni K, O'Reilly U-M. Likely to stop? Predicting stopout in massive open online courses. arXiv preprint arXiv:14083382. 2014.

      [29] Whitehill J, Williams JJ, Lopez G, Coleman CA, Reich J. Beyond prediction: First steps toward automatic intervention in MOOC student stopout. 2015.

      [30] Wang F, Chen L, editors. A Nonlinear State Space Model for Identifying At-Risk Students in Open Online Courses. EDM; 2016.

      [31] Meier Y, Xu J, Atan O, Schaar Mvd. Predicting Grades. IEEE Transactions on Signal Processing. 2016;64(4):959-72.

      [32] Hone KS, El Said GR. Exploring the factors affecting MOOC retention: A survey study. Computers & Education. 2016;98:157-68.

      [33] Kloft M, Stiehler F, Zheng Z, Pinkwart N, editors. Predicting MOOC dropout over weeks using machine learning methods. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs; 2014.

      [34] Tsironis A, Katsanos C, Xenos M, editors. Comparative usability evaluation of three popular MOOC platforms. Global Engineering Education Conference (EDUCON), 2016 IEEE; 2016: IEEE.

      [35] Sa'don N, Alias R, Ohshima N. Stakeholders' view on MOOCs sustainability in Malaysian higher education: A preliminary study2015. 18104-12 p.

      [36] Gunawardena A. BRIEF SURVEY OF ANALYTICS IN K12 AND HIGHER EDUCATION2017. 24 p.

      [37] Miguel J, Caballé S, Prieto J, editors. Providing Information Security to MOOC: Towards effective student authentication. Intelligent Networking and Collaborative Systems (INCoS), 2013 5th International Conference on; 2013: IEEE.

      [38] Veeramachaneni K, Dernoncourt F, Taylor C, Pardos Z, O’Reilly U-M, editors. Moocdb: Developing data standards for mooc data science. AIED 2013 Workshops Proceedings Volume; 2013.

      [39] Mohamad N, Ahmad N, Sulaiman S. Data pre-processing: A case study in predicting student’s retention in MOOC. Journal of Fundamental and Applied Sciences. 2017;9(4S):598-613.

      [40] Alias UF, Ahmad NB, Hasan S, editors. Mining of E-learning behavior using SOM clustering. Student Project Conference (ICT-ISPC), 2017 6th ICT International; 2017: IEEE.

      [41] Baker RS, Inventado PS. Educational data mining and learning analytics. Learning analytics: Springer; 2014. p. 61-75.

      [42] Balakrishnan G, Coetzee D. Predicting student retention in massive open online courses using hidden markov models. Electrical Engineering and Computer Sciences University of California at Berkeley. 2013.

      [43] Brooks CA, Thompson C, Teasley SD, editors. Towards A General Method for Building Predictive Models of Learner Success using Educational Time Series Data. LAK Workshops; 2014.

      [44] Nagrecha S, Dillon JZ, Chawla NV, editors. MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable. Proceedings of the 26th International Conference on World Wide Web Companion; 2017: International World Wide Web Conferences Steering Committee.

      [45] Noh K-S. Plan for vitalisation of application of big data for e-learning in South Korea. Indian Journal of Science and Technology. 2015;8(S5):149-55.

    6. "Times New Roman","serif";mso-fareast-font-family:Calibri;color:#111111;
    7. mso-ansi-language:EN-CA;mso-fareast-language:KO;mso-bidi-language:AR-SA'>
    8. style='mso-element:field-end'>

  • Downloads

  • How to Cite

    Mohamad, N., Bahiah Ahmad, N., & Norhayati Abang Jawawi, D. (2018). Malaysia MOOC: Improving Low Student Retention with Predictive Analytics. International Journal of Engineering & Technology, 7(2.29), 398-405. https://doi.org/10.14419/ijet.v7i2.29.13662

    Received date: 2018-06-04

    Accepted date: 2018-06-04

    Published date: 2018-05-22