The Classification Of The Probability Unit 
Ability Levels Of The Eleventh Grade 
Turkish Students By Cluster Analysis


In this study, the probability unit ability levels of the eleventh grade Turkish students were classified through cluster analysis. The study was carried out in a high school located in Trabzon, Turkey during the fall semester of the 2011-2012 academic years. A total of 84 eleventh grade students participated. Students were taught about permutation, combination, binomial expansion, and probability, which were the sub-topics of probability unit, in an individualized mathematics learning environment called UZWEBMAT. After students completed the learning of each sub-topic, they were subjected to an exam about the relevant topic through UZWEBMAT-CAT. Students participated in 5 separate exams (i.e. one for each sub-topic and one end-of-unit test). Data were collected via system records made up of the ability levels of students concerning each subject. The ability levels obtained from each exam were analyzed through hierarchical clustering. According to the study results, the ability levels of students gathered in two main clusters in every test: medium ability level and advanced ability level.

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  • 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.
  • He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(2013), 90–102.
  • Jovanovica, M., Vukicevica, M., Milovanovica, M., & Minovica, M. (2012). Using data mining on student behavior and cognitive style data for improving e-learning systems: A case study. International Journal of Computational Intelligence Systems, 5(3), 597–610.
  • Klosgen, W., & Zytkow, J. (2002). Handbook of data mining and knowledge discovery.
  • New York: Oxford University Press. Kotsiantis, S., & Pintelas, P. (2005). Predicting students marks in hellenic open university. In ICALT’05: The fifth international conference on advanced learning technologies. Kaohsiung, Taiwan (pp. 664–668).
  • Kotsiantis, S., Patriarcheas, K., Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education.
  • Knowledge-Based Systems, 23(2010), 529–535.
  • Kreitzber, C. B., Stocking, M. L. & Swanson, L. (1978). Computerized adaptive testing:
  • Principles and directions. Computers & Education, 2, 319-329. Lazcorreta, E., Botella, F., & Fernández-Caballero, A. (2008). Towards personalized recommendation by two-step modified apriori data mining algorithm. Expert Systems with Applications, 35(3), 1422–1429.
  • Lee, M.W., Chen, S.Y., Chrysostomou, K., & Liu, X. (2009). Mining students’ behavior in web-based learning programs. Expert Systems with Applications, 36(2009) 3459-3464.
  • Lee, Y-J. (2012). Developing an efficient computational method that estimates the ability of students in a Web-based learning environment. Computers & Education, 58(2012), 579–589.
  • Levy, S.T., Wilensky, U. (2011). Mining students’ inquiry actions for understanding of complex systems. Computers & Education, 56(2011), 556–573.
  • Liao, S., Chu, P., & Hsiao, P. Y. (2012). Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems with Applications, 39(2012), 11303–11311.
  • Mostow, J., & Beck, J. (2006). Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, 12(2), 195–208.
  • Nandeshwar, A, Menzies, T., & Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(2011), 14984–14996.
  • Ozyurt, O., Ozyurt, H., & Baki, A. (2013). Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching-learning of probability unit: Details of UZWEBMAT. Expert Systems with Applications, 40(8), 2914- 29
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005.
  • Expert Systems with Applications, 33(2007), 135–146.
  • Pal, S. (2012). Mining educational data to reduce dropout rates of engineering students.
  • International Journal of Information Engineering and Electronic Business, 2(1), 1–7. 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.
  • Romero, C., Ventura S., & Garcia E. (2008). Data mining in course management systems:
  • Moodle case study and tutorial. Computers & Education, 51(2008), 368–384.
  • 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.
  • Tsantis, L., & Castellani, J. (2001). Enhancing learning environments through solution- based knowledge discovery tools. Journal of Special Education Technology, 16(4), 1–35.
  • Vandamme, J.P., Meskens, N. & Superby, J.F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405-419.
  • Wang, Y-H., & Liao, H-C. (2011). Data mining for adaptive learning in a TESL-based e- learning system. Expert Systems with Applications, 38(2011), 6480–6485.
  • Weiss, D. J. (1985). Adaptive testing by computer. Journal of Consulting and Clinical Psychology, 53(6), 774-789.
  • Zafra, A., Romero, C., & Ventura S. (2011). Multiple instance learning for classifying students in learning management systems. Expert Systems with Applications, 38(2011), 15020, 15031.