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Reseach Article

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

by Oyerinde O. D., Chia P. A.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 4
Year of Publication: 2017
Authors: Oyerinde O. D., Chia P. A.

Oyerinde O. D., Chia P. A. . Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression. International Journal of Computer Applications. 157, 4 ( Jan 2017), 37-44. DOI=10.5120/ijca2017912671

@article{ 10.5120/ijca2017912671,
author = { Oyerinde O. D., Chia P. A. },
title = { Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 4 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 37-44 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017912671 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:03:03.883571+05:30
%A Oyerinde O. D.
%A Chia P. A.
%T Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 4
%P 37-44
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management..The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos, Nigeria.Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance.

  1. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
  2. Baker, R. &Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining (JEDM), 1(1), 3–17.
  3. Papamitsiou, Z., & Economides, A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17 (4), 49–64.
  4. Aziz, A. A., Ismail, N. H., & Ahmad, F. (2013). Mining Students' Academic Performance. Journal of Theoretical & Applied Information Technology, 53(3).
  5. Sachin, R. B., & Vijay, M. S. (2012). A survey and future vision of data mining in educational field. In Advanced Computing & Communication Technologies (ACCT), 2012 Second International Conference on (pp. 96-100). IEEE.
  6. Arsad, P. M., Buniyamin, N., Ab Manan, J. L., &Hamzah, N. (2011). Proposed academic students' performance prediction model: A Malaysian case study. In Engineering Education (ICEED), 2011 3rd International Congress on (pp. 90-94). IEEE.
  7. Hämäläinen, W., Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In international conference in intelligent tutoring systems, Taiwan, 525-534.
  8. Romero, C., Ventura, S., Hervás, C., Gonzales, P. (2008). Data mining algorithms to classify students. In International Conference on Educational Data Mining, Montreal, Canada, 8-17.
  9. Minaei-bidgoli, B., Kashy, D.A., Kortmeyer, G., Punch, W.F. (2003). Predicting student performance: an application of data mining methods with an educational Web-based system. In International Conference on Frontiers in Education, 13-18.
  10. Ibrahim, Z., Rusli, D. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In Annual SAS Malaysia Forum, Kuala Lumpur, 1-6.
  11. Haddawy, P., Thi, N., Hien, T.N. (2007). A decision support system for evaluating international student applications. In Frontiers In Education Conference, Milwaukee, 1-4.
  12. Pardos, Z., Heffernan, N., Anderson, B., Heffernan, C. (2007). The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks. In International Conference on User Modeling, Corfu, Greece, 435-439.
  13. Pardos, Z., Beck, J.E., Ruiz, C., Heffernan, N. (2008). The Composition Effect: Conjunctive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS. In International Conference on Educational Data Mining, Montreal, 147-156.
  14. Stevens, R., Giordani, A., Cooper, M., Soller, A., Gerosa, L., Cox, C. (2005). Developing a Framework for Integrating Prior Problem Solving and Knowledge Sharing Histories of a Group to Predict Future Group Performance. In International Conference on Collaborative Computing: Networking, Applications and Work sharing, Boston, 1-9.
  15. Ayers E., Junker B.W. (2006). Do skills combine additively to predict task difficulty in eighth grade mathematics? In AAAI Workshop on Educational Data Mining: Menlo Park, 14-20.
  16. Desmarais, M.C., Gagnon, M., Meshkinfram, P. (2006). Bayesian Student Models Based on Item to Item Knowledge Structures In Conference on Technology Enhanced Learning, Crete, Greece, 1-10.
  17. Gedeon, T.D., Turner, H.S. (1993). Explaining student grades predicted by a neural network. In International conference on Neural Networks, Nagoya, 609-612.
  18. Wang, T., &Mitrovic, A. (2002). Using neural networks to predict student's performance. In Computers in Education, 2002. Proceedings. International Conference on (pp. 969-973). IEEE.
  19. Fausett, L.V., Elwasif, W. (1994). Predicting performance from test scores using back propagation and counter propagation. In IEEE World Congress on Computational Intelligence, Paris, France, 3398–3402
  20. Delgado, M., Gibaja, E., Pegalajar, M.C., Pérez, O. (2006). Predicting Students' Marks from. Moodle Logs using Neural Network Models. In International Conference on Current Developments in Technology-Assisted Education, Sevilla, Spain, 586-590.
  21. Oladokun, V.O., Adebanjo, A.T., Charles-owaba, O.E. (2008). Predicting student’s academic performance using artificial neural network: A case study of an engineering course. In Pacific Journal of Science and Technology, 9,1, 72-79.
  22. Nebot, A., Castro, F., Vellido, A., Mugica, F. (2006). Identification of fuzzy models to predict studentsperfornance in an e-learning environment. In International Conference on Web-based Education, Puerto Vallarta, 74-79.
  23. Chen, C., Chen, M., Li, Y. (2007). Mining key formative assessment rules based on learner portfiles for web-based learning systems. In IEEE International Conference on Advanced Learning Technologies, Japan, 1-5.
  24. Ogor, E.N. (2007). Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques. In Electronics, Robotics and Automotive Mechanics Conference, Washington, DC, 354-359.
  25. Shangping, D., & Ping, Z. (2008, April). A data mining algorithm in distance learning. In Computer Supported Cooperative Work in Design, 2008. CSCWD 2008. 12th International Conference on (pp. 1014-1017). IEEE.
  26. Zafra, A., Ventura, S. (2009), Predicting student grades in learning management systems with multiple instance programming. In International Conference on Educational Data Mining, Cordoba, Spain, 307-314.
  27. Chan, C.C. (2007). A Framework for Assessing Usage of Web-Based eLearning Systems. In International Conference on innovative Computing, Information and Control, Washington, DC, 147- 151.
  28. Etchells, T.A., Nebot, A., Vellido, A., Lisboa, P.J.G., Mugica, F. (2006). Learning what is important: feature selection and rule extraction in a virtual course. In European Symposium on Artificial Neural Networks, Brussels, Belgium, 401-406.
  29. Kotsiantis, S.B., Pintelas, P.E., (2005). Predicting Students' Marks in Hellenic Open University. In IEEE international Conference on Advanced Learning Technologies, Washington, DC, 664-668.
  30. Anozie, N., Junker, B.W. (2006). Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system. In Educational Data Mining AAAI Work-shop, California, 1-6
  31. Yu, C.H., Jannasch-pennell, A., Digangi, S., Wasson, B. (1999). Using On-line interactive statistics for evaluating Web-based instruction, In Journal of Educational Media International, 35, 157-161.
  32. Golding, P., Donalson, O. (2006). Predicting Academic Performance. In Frontiers in Education Conference. San Diego, California, 21-26.
  33. Arnold, A., Scheines, R., Beck, J.E., Jerome, B. (2005). Time and Attention: Students, Sessions, and Tasks. In AAAI2005 Workshop on Educational Data Mining, Pittsburgh, 62-66.
  34. Martinez, D. (2001). Predicting Student Outcomes Using Discriminant Function Analysis.
  35. Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251-269.
  36. Myller, N., Suhonen, J., Sutinen, E. (2002). Using Data Mining for Improving Web-Based Course Design. In International Conference on Computers in Education, Washington, 959- 964.
  37. Beck, J.E., Woolf, B.P. (2000). High-level student modeling with machine learning. In Fifth International Conference on Intelligent Tutoring Systems, Alagoas, Brazil, 584-593.
  38. Freyberger, J., Heffernan, N.T., Ruiz, C. (2004). Using Association Rules to Guide a Search for Best Fitting Transfer Models of Student Learning. In Workshop Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, Alagoas, Brazil, 1-10.
  39. Cetintas, A., Si, L., Xin, Y.P., Hord, C. (2009). Predicting correctness of problem solving from low-level log data in intelligent tutoring systems. In International Conference on Educational Data Mining, Cordoba, Spain, 230-238.
  40. Feng, M., Heffernan, N., Koedinger, K. (2005). Looking for sources of error in predicting student’s knowledge. In: AAAI’05 workshop on Educational Data Mining, 1-8.
  41. Wang, A.Y., Newlin, M.H. (2002). Predictors of web-based performance: the role of self-efficacy and reasons for taking an on-line class. In Computers in Human Behavior Journal, 18, 151-163.
  42. Pritchard, D., Warnakulasooriya, R. (2005). Data from a Web-based Homework Tutor can predict Student’s Final Exam Score. In World Conference on Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, 2523-2529.
  43. Mcdonald, B., (2004). Predicting student success. In Journal for Mathematics Teaching and Learning,1-14.
  44. Draper, N.R., Smith, H. (1998). Applied Regression Analysis. Wiley.
Index Terms

Computer Science
Information Sciences


Learning Analytics Educational Data Mining Students Academic Performance Multiple Linear Regression.