The Classification Of The Probability Unit Ability Levels Of The Eleventh Grade Turkish Students By Cluster Analysis
Abstract
Keywords
Computerized Adaptive Testing, Individual Differences, Ability Level, Hierarchical Cluster Analysis.
References
- 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–
- 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.
- Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
- Chang, L. (2006). Applying data mining to predict college admissions yield: A case study.
- New Directions for Institutional Research, 2006(131), 53–68.
- Chen, S.Y., & Liu, X. (2011): Mining students' learning patterns and performance in Web- based instruction: a cognitive style approach. Interactive Learning Environments, 19(2), 179-192.
- Chen, C., Hsieh, Y., & Hsu, S. (2007). Mining learner profile utilizing association rule for web-based learning diagnosis. Expert Systems with Applications, 33(1), 6-22.
- Falakmasir, M.H, & Jafar, H. (2010). Using educational data mining methods to study the impact of virtual classroom in e-learning. Paper presented at the Proceedings of the 3rd
- International Conference on Educational Data Mining, Pittsburgh, PA, USA. Fausett, L., & Elwasif, W. (1994). Predicting performance from test scores using backpropagation and counterpropagation. In WCCI’94: IEEE world congress on computational intelligence. Washington, USA (pp. 3398–3402).
- 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.