The State of the Art on Educational Data Mining in Higher Education

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-31 Number-1
Year of Publication : 2016
Authors : Mohamed Osman Hegazi, Mazahir Abdelrhman Abugroon
  10.14445/22312803/IJCTT-V31P109

MLA

Mohamed Osman Hegazi, Mazahir Abdelrhman Abugroon "The State of the Art on Educational Data Mining in Higher Education". International Journal of Computer Trends and Technology (IJCTT) V31(1):46-56, January 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Educational data mining (EDM) is a broader term that focuses on analyzing, exploring, predicting, clustering, and classification of data in educational institutions. EDM grows faster and covers many interdisciplinary such as education, elearning, data mining, data analysis, intelligent system etc... The paper presents most relevant work in the area of EDM in higher education it covers course management systems, student behaviors, decision support system, and student retention and attrition. The paper also provide a comparison study between some of research work in such areas. Because of the growth in the interdisciplinary nature of EDM the paper, also try to provide boundary scope and definitions for EDM.

References
[1] The Educational Data Mining community website, www.educationaldatamining.org
[2] Barnes, T., Desmarais, M., Romero, C., Ventura, S. (2009). Educational Data Mining 2009: 2nd International Conference on Educational Data Mining, _Proceedings. Cordoba, Spain.
[3] Campbell, J., & Oblinger, D. (2007). Academic analytics. Washington, DC: Educause.
[4] Baker, R., & Yacef, K. (2009). The State of Educational Data mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1
[5] Berson, A., Smith, S., & Thearling, K. (2011). An Overview of Data Mining Techniques Retrieved November 28, 2011, from
[6] Arockiam, L., S. Charles, and M. Amala Jayanthi. "An Impact of Emotional Happiness and Personality in Students’ Learning Environment." Data Mining and Knowledge Engineering 7.2 (2015): 69-74.
[7] Chalaris, Manolis, et al. "Examining students' graduation issues using data mining techniques-The case of TEI of Athens." INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings of the 4th International Conference on Integrated Information. Vol. 1644. AIP Publishing, 2015.
[8] Foltz, Peter W., and Mark Rosenstein. "Analysis of a Large-Scale Formative Writing Assessment System with Automated Feedback." Proceedings of the Second (2015) ACM Conference on Learning@ Scale. ACM, 2015.
[9] Ocumpaugh, Jaclyn, et al. "Population validity for Educational Data Mining models: A case study in affect detection." British Journal of Educational Technology 45.3 (2014): 487-501.
[10] Patidar, Preeti, Jitendra Dangra, and M. K. Rawar. "Decision Tree C4. 5 algorithm and its enhanced approach for Educational Data Mining." (2015).
[11] Yamamoto, Yukiko, et al. "Increasing the Sensitivity of a Personalized Educational Data Mining Method for Curriculum Composition." Emerging Issues in Smart Learning. Springer Berlin Heidelberg, 2015. 201-208.
[12] Romero, Cristóbal, and Sebastián Ventura. "Educational data mining: a review of the state of the art." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40.6 (2010): 601-618.
[13] Rabbany, Reihaneh, Mansoureh Takaffoli, and Osmar R. Zaïane. "Analyzing participation of students in online courses using social network analysis techniques." Proceedings of educational data mining. 2011.
[14] Romero, Cristóbal, Sebastián Ventura, and Enrique García. "Data mining in course management systems: Moodle case study and tutorial." Computers & Education 51.1 (2008): 368-384.
[15] Dringus, Laurie P., and Timothy Ellis. "Using data mining as a strategy for assessing asynchronous discussion forums." Computers & Education 45.1 (2005): 141-160.
[16] Castro, Félix, A. Nebot, and Francisco Mugica. "EXTRACTION OF LOGICAL RULES TO DESCRIBE STUDENTS’LEARNING BEHAVIOR."Proceedings of the sixth conference on IASTED International Conference Web-Based Education. Vol. 2. 2007.
[17] Zorrilla, Marta E., et al. "Web usage mining project for improving web-based learning sites." Computer Aided Systems Theory–EUROCAST 2005. Springer Berlin Heidelberg, 2005. 205-210.
[18] Wang, Ya-Huei, and Hung-Chang Liao. "Data mining for adaptive learning in a TESL-based e-learning system." Expert Systems with Applications 38.6 (2011): 6480-6485.
[19] Beck, J.E., Woolf, B.P.: High-Level Student Modeling with Machine Learning. In: Gauthier, G., et al. (eds.): Intelligent Tutoring Systems, ITS 2000. Lecture Notes in Computer Science, Vol. 1839. Springer, Berlin Heidelberg New York (2000) 584-593
[20] Blikstein, P. (2011). Using learning analytics to assess students' behavior in open – ended programming tasks. Paper presented at the Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, Canada.
[21] Huang, Jen-Peng, Show-Ju Chen, and Huang-Cheng Kuo. "An efficient incremental mining algorithm- QSD." Intelligent Data Analysis 11.3 (2007): 265-278.
[22] Calders, Toon, and Mykola Pechenizkiy. "Introduction to the special section on educational data mining." ACM SIGKDD Explorations Newsletter 13.2 (2012): 3-6.
[23] Guan, J., Nunez, W., & Welsh, J. (2002). Institutional strategy and information support: the role of data warehousing in higher education. Campus-Wide Information Systems,19(5)-164- 174.6. Chacon, F., Spicer, D., & Valbuena, A. (2012). Analytics in Support of Student Retention and Success (Research Bulletin 3, 2012ed.). Louisville, CO: Educause Center for Applied Research.
[24] Chau, Vo Thi Ngoc, and Nguyen Hua Phung. "A knowledge-driven educational decision support system." Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012 IEEE RIVF International Conference on. IEEE, 2012.
[25] Deniz, Dervis Z., and Ibrahim Ersan. "An Academic Decision Support System Based on Academic Performance Evaluation for Student and Program Assessment." International Journal of Engineering Education 18.2 (2002): 236-244.
[26] Feghali, Tony, Imad Zbib, and Sophia Hallal. "A webbased decision support tool for academic advising." Journal of Educational Technology & Society14.1 (2011): 82-94.
[27] Kotsiantis, Sotiris B. "Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades."Artificial Intelligence Review 37.4 (2012): 331-344.
[28] Nagy, Heba Mohammed, Walid Mohamed Aly, and Osama Fathy Hegazy. "An Educational Data Mining System for Advising Higher Education Students." World Acad. Sci. Eng. Technol. Int. J. Inf. Sci. Eng 7.10 (2013): 175-179.
[29] Vinnik, Svetlana, and Marc H. Scholl. UNICAP: Efficient decision support for academic resource and capacity management. Springer Berlin Heidelberg, 2005.
[30] Bose, Ranjit, and Vijayan Sugumaran. "Application of intelligent agent technology for managerial data analysis and mining." ACM SIGMIS Database 30.1 (1999): 77-94.
[31] Lee, Jang Hee, and Sang Chan Park. "Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce." Expert Systems with Applications 25.4 (2003): 619-635.
[32] Chrysostomou, K., Chen, S. Y., & Liu, X. (2009). Investigation of Users' Preferences in Interactive Multimedia Learning Systems: A Data Mining Approach. Interactive Learning Environments, 17(2), 151-163.
[33] Lee, Jang Hee, and Sang Chan Park. "Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce." Expert Systems with Applications 25.4 (2003): 619-635.
[34] Luan, J. (2002). Data Mining and Knowledge Management in Higher Education-potential Applications. Paper presented at the Annual Forum for the Association for Institutional Research, Toronto, Ontario, Canada.9. Lin, S.-H. (2012). Data mining for student retention management. J. Comput. Sci. Coll., 27 (4) 92-99.
[35] Chacon, Fabio, Donald Spicer, and A. Valbuena. "Analytics in support of student retention and success." Research Bulletin 3 (2012): 1-9.
[36] Yeats, Rowena, et al. "What a difference a writing centre makes: a small scale study." Education+ Training 52.6/7 (2010): 499-507.
[37] Ohri, Zinnia. "A Critical Analysis of Various Data Mining Techniques in Educational Assessment and Feedback." IITM Journal of Information Technology: 25.
[38] Su, Jun-Ming, et al. "A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments." User modeling and useradapted interaction 21.1-2 (2011): 5-49.
[39] Tsantis, L., Castellani, J. (2001). Enhancing learning environments through solution-based knowledge discovery tools. In Journal of Special Education Technology, 16,4, 39-52
[40] Pahl, C., Donnellan, D.: Data Mining Technology for the Evaluation of Webbased Teaching and Learning Systems. In: World Conference on e-Learning in Corp., Govt., Health., & Higher Education. (2002) 747-752
[41] Chu, K., Chang, M., Hsia, Y.: Designing a Course Recommendation System on Web based on the Students’ Course Selection Records. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications (2003) 14-21
[42] Chang, K., Beck, J., Mostow, J., Corbett, A.: A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems. In: Ikeda, M., et al. (eds.): 8th International Conference on Intelligent Tutoring Systems, ITS2006. LNCS Vol. 4053. Springer, Berlin Heidelberg New York (2006) 104-113

Keywords
Data Mining , DM, Educational Data Mining, EDM, Knowledge Discovery, KDD, Decision Support System, DSS, Course Management Systems, CMS.