Main Article Content
Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in IJIER journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
C. Romero, S. Ventura, and P. De Bra. (2004). Knowledge discovery with genetic programming for providing feedback to courseware authors. User Modeling and User-Adapted Interaction, 14(5), 425–464.
C. Romero, S. Ventura, M. Pechenizkiy, and R. S. Baker. (2010). Handbook of educational data mining. CRC Press.
C. Romero, S. Ventura, P. G. Espejo, and C. Herv´as. (2008). Data mining algorithms to classify students. EDM, 8-17.
C. Tang, R. W. Lau, Q. Li, H. Yin, T. Li, and D. Kilis. (2000). Personalized courseware construction based on web data mining. Web Information Systems Engineering, 2000. Proceedings of the First International Conference, 2, 204–211.
C. Wallace, K. B. Korb, and H. Dai. (1996). Causal discovery via mml. ICML, 96, 516–524.
Chan, C. C. (2007). A framework for assessing usage of web-based e-learning System. Innovative Computing, Information and Control, 147–147.
F. Castro, A. Vellido, A. Nebot, and F. Mugica. (2007). Applying data mining techniques to e-learning problems. Evolution of teaching and learning paradigms in intelligent environment, 183–221.
H. Jeong and G. Biswas. (2008). Mining student behavior models in learning-by-teaching environments. EDM, 127–136.
J. Chen, Q. Li, L. Wang, and W. Jia. (2004). Automatically generating an e-textbook on the web. Advances in Web-Based Learning–ICWL 2004, 35-42.
J. Mostow and J. Beck. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. Intelligent tutoring systems, 353–362.
J. Tane, C. Schmitz, and G. Stumme. (2004). Semantic resource management for the web: an e-learning application. Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, 1–10.
L. P. Dringus and T. Ellis. (2005). Using data mining as a strategy for assessing asynchronous discussion forums. Computers & Education, 45(1), 141–160.
M. Cocea, A. Hershkovitz, and R. S. Baker. (2009). The impact of off-task and gaming behaviors on learning: immediate or aggregate?
Muehlenbrock, M. (2005). Automatic action analysis in an interactive learning environment. Proceedings of the 12 th International Conference on Artificial Intelligence in Education, 73–80.P. Reyes and P. Tchounikine. (2005). Mining learning groups’ activities in forum-type tools. Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!, 509–513.
R. S. Baker and K. Yacef. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3-17.
S. K. Mohamad and Z. Tasir. (2013). Educational data mining: A review. Procedia-Social and Behavioral Sciences, 97, 320–324.
Scott, J. (2012). Social network analysis. Sage.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54.
Ueno, M. (2004). Data mining and text mining technologies for collaborative learning in an ilms. Proceedings. IEEE International Conference, 1052–1053.
Universities. (2018). Retrieved June 19, 2018, from University Grants Commission - Sri Lanka: http://ugc.ac.lk/en/universities-and-institutes/universities.html
V. J. Hodge and J. Austin. (2014). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126.