Using data mining for predicting relationships between online question theme and final grade

Educational Technology and Society

Volumn 15, Issue 3, 2012, Pages 77-88

  Abdous, M'hammed ^a   He, Wu ^a   Yen, Cherng Jyh ^a  

^a OLD DOMINION UNIVERSITY   (United States)

Author keywords

Clustering analysis; Data mining; Educational data mining; Live video streaming

Indexed keywords

EID: 84873845149     PISSN: 11763647     EISSN: 14364522     Source Type: Journal    
DOI: None     Document Type: Article

References (41)

1. Abdous, M., & He, W. (2011). Using text mining to uncover students' technology-related problems in live video streaming. British Journal of Educational Technology, 42(1), 40-49.
2. Abdous, M., & Yen, C.-J. (2010). A predictive study of learner satisfaction and outcomes in face-to-face, satellite broadcast, and live video-streaming learning environments. The Internet and Higher Education, 13, 248-257.
3. Anand Kumar N, & Uma, G. (2009). Improving academic performance of students by applying data mining technique. EuroJournals, 34, pp. 526-534.
4. Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching & Learning, 4(2), 1-9.
5. Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions 2009. Journal of Education Data Mining, 1(1), 3-17. Retrieved from http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf
6. Ba-Omar, H., Petrounias, I., & Anwar, F. (2007). A framework for using web usage mining to personalise e-learning. In M. Spector et al. (Eds.), Proceedings of the Seventh IEEE International Conference on Advanced Learning Technologies. ICALT 2007 (pp.937-938). Los Alamitos, California: IEEE Computer Society Press
7. Black, E., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in an online course. Internet and Higher Education, 11(2), 65-70.
8. Burr, L., & Spennemann, D. H. R. (2004). Patterns of user behaviour in university online forums. International Journal of Instructional Technology and Distance Learning, 1(10), 11-28.
9. Castellano, E., & Martínez, L. (2008, July). ORIEB, A CRS for academic orientation using qualitative assessments. Paper presented at the IADIS International Conference E-Learning, Amsterdam, The Netherlands.
10. Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In L. C. Jain, R. Tedman, & D. Tedman (Eds.), Evolution of Teaching and Learning Paradigms in Intelligent Environment (pp. 183-221). New York: Springer-Verlag.
11. Chang, L. (2006). Applying data mining to predict college admissions yield: A case study. New Directions for Institutional Research, 2006(131), 53-68.
12. Chen, S. Y., & Liu, X. (2004). The contribution of data mining to information science. Journal of Information Science, 30(6), 550-558.
13. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
14. Delavaria, N., Phon-Amnuaisuka, S., & Reza Beikzadehb, M. (2008). Data mining application in higher learning institutions. Informatics in Education, 7(1), 31.
15. Dringus, L. P., & Ellis, T. (2005). Using data mining as a strategy for assessing asynchronous discussion forums. Computers and Education, 45(1), 141-160.
16. Falakmasir, M., & Jafar, H. (2010, June). Using educational data mining methods to study the impact of virtual classroom in elearning. Paper presented at the Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA.
17. Faulkner, R., Davidson, J. W., & McPherson, G. E. (2010). The value of data mining in music education research and some findings from its application to a study of instrumental learning during childhood. International Journal of Music Education, 28(3), 212-30.
18. Garcia, E., Romero, C., Ventura, S., & de Castro, C. (2011). A collaborative educational association rule mining tool. Internet and Higher Education, 14(2), 77-88.
19. Harding, J. A., Shahbaz, M., Srinivas, M., and Kusiak, A. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128(4), 969-976.
20. Hen, L. E., & Lee, S. P. (2008). Performance analysis of data mining tools cumulating with a proposed data mining middleware. Journal of Computer Science, 4(10), 826-833.
21. Herbert, M. (2008). Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration, 9(4). Retrieved from http://www.westga.edu/~distance/ojdla/winter94/herbert94.htm
22. Hosmer, D.W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.
23. Hung, J., & Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching, 4(4). Retrieved from http://jolt.merlot.org/vol4no4/hung_1208.htm
24. Hung, J.-L., & Crooks, S. M. (2009). Examining online learning patterns with data mining techniques in peer-moderated and teacher-moderated courses. Journal of Educational Computing Research, 40(2), 183-210.
25. King, J. E. (2008). Binary logistic regression. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 358-384). Thousand Oaks, CA: Sage Publications.
26. Lin, F.-R., Hsieh, L.-S., & Chuang, F.-T. (2009). Discovering genres of online discussion threads via text mining. Computers & Education, 52(2), 481-495.
27. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early warning system" for educators: A proof of concept. Computers & Education, 54(2), 588-599.
28. rd ASEE/IEEE Frontiers in Education Conference, Boulder, CO, USA. Retrieved from http://lon-capa.org/papers/v5-FIE-paper.pdf
29. Ngai, E. W. T., Xiu, L., and Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature relationship and classification. Expert Systems with Applications, 36, 2529-2602.
30. Norusis, M. (2008). SPSS statistics 17.0 advanced statistical procedures companion. Upper Saddle River, NJ: Prentice Hall.
31. O'Connell, A. (2006). Logistic regression models for ordinal response variables. Thousand Oaks, CA: Sage Publications.
32. Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaiane, O. (2009). Clustering and sequential data mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759-772.
33. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 40(6), 601-618.
34. Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2010). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education. doi: 10.1002/cae.20456
35. Romero, C., Ventura, S., & Garcia, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
36. Sun, P. C., Cheng, H. K., Lin, T. C., & Wang, F. S. (2008). A design to promote group learning in e-learning: experiences from the field. Computers & Education, 50(3), 661-677.
37. Vialardi, C., Chue, J., Peche, J., Alvarado, G., Vinatea, B., Estrella, J., & Ortigosa, L. (2011). A data mining approach to guide students through the enrollment process based on academic performance. User Modeling and User - Adapted Interaction, 21(1-2), 217.
38. Zafra, A., & Ventura, S. (2009, June). Predicting student grades in learning management systems with multiple instance programming. Paper presented at the Proceedings of the 2nd International Conference on Educational Data Mining, Cordoba, Spain.
39. Zaïane, O. R. (2002). Building a recommender agent for e-learning systems. In L. Aroyo et al. (Eds.), Proceedings of the International Conference on Computers in Education ICCE '02 (55-59). Washington, DC: IEEE Computer Society Press
40. Zaiane, O. R., & Luo, J. (2001). Towards evaluating learners' behavior in a web-based distance learning environment. Retrieved from http://webdocs.cs.ualberta.ca/~zaiane/postscript/icalt.pdf
41. Zha, S., Kelly, P., Park, M. K., & Fitzgerald, G. (2006). An investigation of ESL students using electronic discussion boards. Journal of Research on Technology in Education, 38(3), 349-367.