Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Chunyan Yu This email address is being protected from spambots. You need JavaScript enabled to view it.1

1School of Computer and Information Engineering, Chuzhou University, P.R. China


Received: April 9, 2018
Accepted: April 30, 2018
Publication Date: June 1, 2018

Download Citation: ||  


Learning analysis is one of the most important applications of machine learning. Many studies have proposed solutions to learning performance prediction using online learning data. Unlike the previous studies, this paper analyzes online learning environment and formalizes the problem of online learning prediction. Based on the formalization, a multi-feature based learning prediction model for SPOC is proposed, called SPOC-MFLP, which generalizes the prediction problem of SPOC learning including objective, constraints, system and algorithms. The proposed SPOC-MFLP could be extended for MOOC and other online learning forms. Principle components analysis is adopted to discover the correlations of students’online multi features, and linear regression and deep neural network are used to predict the learning performance. The predicted results include specific scores or segmented grades of the final exam of SPOC, as well as students’future specialized courses. Experimental data are collected from a SPOC in Chuzhou University for two years and the experimental results reveal that the proposed SPOC-MFLP performs well.

Keywords: Machine Learning, Learning Analysis, Features Analysis, SPOC, Learning Performance Prediction


  1. [1] Goldberg, D. E. and Holland, J. H., “Genetic Algorithms and Machine Learning,” Machine Learning, Vol. 3,No. 2,pp. 9599(1988). doi:10.1007/BF00113892
  2. [2] Witten, I. H., Frank, E., Hall, M. A., et al., Data Mining: Practical Machine Learning Tools and Techniques,4 th ed., Morgan Kaufmann, Boston, pp. 79 (2016).
  3. [3] Mcauley, A., Stewart, B., Cormier, D., et al., The MOOC Model for Digital Practice (2010).
  4. [4] Fox, A., “From MOOCs to SPOCs,” Communications of the Acm, Vol. 56, No. 12, pp. 3840 (2013). doi: 10. 1145/2535918
  5. [5] Siemens, G. and d Baker, R. S. J., “Learning Analytics and Educational Data Mining: Towards Communication and Collaboration,” Proc. of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada, April 29May 02, pp. 252254 (2012).
  6. [6] Peña-Ayala,A., “Learning Analytics:  aGlance of Evolution, Status, and Trends According to a Proposed Taxonomy,”Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery, Vol. 8, No. 3, pp. 129 (2018). doi: 10.1002/widm.1243
  7. [7] Baltrušaitis, T., Ahuja, C. and Morency, L. P., “Multimodal Machine Learning: a Survey and Taxonomy,” IEEE Transactions on Pattern Analysis &Machine Intelligence, pp. 120 (2018). doi: 10.1109/TPAMI.2018. 2798607
  8. [8] Klaus, G., et al., “LSTM: a Search Space Odyssey,” IEEE Transactions on Neural Networks and Learning Systems,” Vol. 28, No. 10, pp. 22222232 (2017). doi: 10.1109/TNNLS.2016.2582924
  9. [9] Qiu, J., Wu, Q., Ding, G., et al., “ASurvey of Machine Learning for Big Data Processing,” Eurasip Journal on Advances in Signal Processing, Vol. 67 (2016). doi: 10.1186/s13634-016-0355-x
  10. [10] Simon,P.,Too Big to Ignore: the Business Case for Big Data, Wiley, New Jersey, pp. 25 (2013).
  11. [11] Ghaffarian, S. M. and Shahriari, H. R., “Software Vulnerability Analysis and Discovery Using Machine learning and Data-mining Techniques: a Survey,” Acm Computing Surveys, Vol. 50, No. 4, pp. 136 (2017). doi: 10.1145/3092566
  12. [12] Buczak, A. L. and Guven, E., “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys & Tutorials, Vol. 18, No. 2, pp. 11531176 (2017). doi: 10.1145/3092566
  13. [13] Dipnall, J. F., Pasco, J. A., Berk, M., et al., “Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression,” Plos One, Vol. 11, No. 2 (2016). doi: 10.1371/ 0148195
  14. [14] Picciano, A. G., “The Evolution of Big Data and Learning Analytics in American Higher Education,” Journal of Asynchronous Learning Networks, Vol. 16, No. 3, pp. 920 (2012).
  15. [15] Ludgate, H., “NMC Horizon Report: 2013 Higher Education Edition,” Austin, Texas: The New Media Consortium (2013).
  16. [16] Johnson, L., Adams Becker, S., Cummins, et al., “NMC Horizon Report: 2016 Higher Education Edition, Austin, Texas: The New Media Consortium (2016).
  17. [17] Shapiro, H. B., Lee, C. H., Roth, N. E. W., et al., “Understanding the Massive Open Online Course (MOOC) Student Experience,” Computers & Education, Vol. 110, pp. 3550 (2017). doi: 10.1016/j.compedu.2017. 03.003
  18. [18] Jiang, Q., Zhao, W., Wang, P. J., et al., “Realization of Individual Adaptive Online Learning Analysis Model Based Big Data,” China Educational Technology, Vol. 336, pp. 8592 (2015).
  19. [19] Wong, J. S., Pursel, B.,Divinsky, A., etal.,“An Analysis of MOOC Discussion Forum Interactions from the Most Active Users,” Proc. of Social Computing, Behavioral-Cultural Modeling, and Prediction, WashingtonDC,USA, March31April3,pp. 452457 (2015).
  20. [20] Smith, M. K., Wood, W. B., Adams, W. K., et al., “Why Peer Discussion Improves Student Performance on In-Class Concept Questions,” Science, Vol. 323, No. 5910, pp. 122124 (2009). doi: 10.1126/science.1165919
  21. [21] Breslow, L., Pritchard, D. E., Deboer, J., et al., “Studying Learning in the Worldwide Classroom Research into edX’s First MOOC,” Research & Practice in Assessment, Vol. 8, pp. 1325 (2013).
  22. [22] Viswanath Venkatesh, H. B. “Technology Acceptance Model 3 and a Research Agenda on Interventions,” Decision Sciences,Vol.39, No. 2,pp. 273315 (2008). doi: 10.1111/j.1540-5915.2008.00192.x
  23. [23] Wang, Z., A Study of the Key Inouential Factors on Effect of E-learning, Master Dissertation, East China Normal University, Shanghai, China (2007).
  24. [24] Tan, G. X., Xu, F. and Qu, W. J., “Influential Factors and Models of Online Teaching Behavior Intention of University Students,” E-Education, Research, Vol. 34, No. 1, pp. 4753 (2012).
  25. [25] Li,D., Li,X. and Shao, P, “Studying on theFactors Related to Learning Success in Mixed Web Based Learning Environment,” China Journal of Information Systems, (2011).
  26. [26] Haney, C. L., Atiq, S. Z., DeBoer, J. and Cox, D., “Comparing Different Learning Activities in a Global Neuroscience MOOC,” Proc. Of ASEE‘s 123rd Annual Conference and Exposition, New Orleans, LA, June 2629 (2016).
  27. [27] Zhao, Z., Wu, Q., Chen, H. and Wan, C., “Learning Quality Evaluation of MOOC Based on Big Data Analysis,” Proc. of International Conference on Smart Computing and Communication, Springer, Cham, December, pp. 277286 (2016).
  28. [28] Saberi, N. and Montazer, G. A., “ANew Approach for Learners’Modeling in E-learning Environment Using LMS Logs Analysis,” Proc. of 2012 Third International Conference on E-Learning and E-Teaching (ICELET), Tehran, Iran, Feb. 1415, pp. 2533 (2012).
  29. [29] Khalil, M. and Ebner, M., “Clustering Patterns of Engagement in Massive Open Online Courses (MOOCs): the Use of Learning Analytics to Reveal Student Categories,” Journal of Computing in Higher Education, Vol. 29, No. 1, pp. 119 (2017). doi: 10.1007/s12528016-9126-9
  30. [30] Wu, Q. and Luo, R., “Predicting the Students’ Performances and Reflecting the Teaching Strategies Based on the E-Learning Behaviors,” Modern Educational Technology, Vol. 27, No. 6, pp. 1824 (2017). doi: 10. 3969/j.issn.1009-8097.2017.06.003
  31. [31] You, J. W., “Identifying Significant Indicators Using LMS Data to Predict Course Achievement in Online Learning,” Internet & Higher Education, Vol. 29, p. 2330 (2016). doi: 10.1016/j.iheduc.2015.11.003
  32. [32] Information on
  33. [33] Keefe, J. W., School Applications of the Learning Style Concept: Student Learning Styles, Reston, VA: National Association of Secondary School Principals, Vol. 44 (1979). [34] Kinsella, K., “Understanding and Empowering Diverse Learners in the ESL Classroom,” Learning Styles in the ESL/EFLClassroom, pp. 170194 (2002).



69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.