Teaching analytics: A clustering and triangulation study of digital library user data

Educational Technology and Society

Volumn 15, Issue 3, 2012, Pages 103-115

  Xu, Beijie ^a   Recker, Mimi ^a  

^a UTAH STATE UNIVERSITY   (United States)

Author keywords

Educational data mining; Latent class analysis; Teacher usage patterns

Indexed keywords

EID: 84873840432     PISSN: 11763647     EISSN: 14364522     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. th ed.). Upper Saddle River, NJ: Pearson.
2. th ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 1-10). New York, NY: ACM. doi=10.1145/1555400.1555402
3. 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.
4. Borgman, C., Abelson, H., Dirks, L., Johnson, R., Koedinger, K., Linn, M., & Szalay, A. (2008). Fostering learning in the networked world: The cyberlearning opportunity and challenge, a 21st century agenda for the national science foundation. Arlington, VA: National Science Foundation. Retrieved from National Science Foundation website: http://www.nsf.gov/pubs/2008/nsf08204/nsf08204.pdf
5. Carlson, B., & Reidy, S. (2004). Effective access: Teachers' use of digital resources (research in progress). OCLC Systems and Services, 20(2), 65-70. doi: 10.1108/10650750410539068
6. Choudhury, S., Hobbs, B., & Lorie, M. (2002). A framework for evaluating digital library services. D-Lib Magazine, 8. Retrieved from http://www.dlib.org/dlib/july02/choudhury/07choudhury.html
7. th IEEE International Conference on Tools with Artificial Intelligence (pp.558-567). Washington, DC: IEEE Computer Society. doi:10.1.1.27.3042
8. Durfee, A., Schneberger, S., & Amoroso, D. L. (2007). Evaluating students' computer-based learning using a visual data mining approach. Journal of Informatics Education Research, 9, 1-28.
9. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215-231.
10. Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255-274.
11. nd ed.). San Francisco, CA: Kaufmann.
12. Howell, D. C. (2007). Statistical methods for psychology. Belmont, CA: Cengage Wadworth.
13. th International Conference on User Modeling (pp. 81-90). New York, NY: Springer.
14. th ACM/IEEE-CS joint conference on Digital libraries (pp. 375-384). New York, NY: ACM. doi: 10.1145/1378889.1378956
15. Kuha, J. (2004). AIC and BIC: Comparisons of assumptions and performance. Sociological Methods and Research, 33(2), 188-229.
16. Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 175-198). Thousand Oakes, CA: Sage.
17. rd International Conference on Educational Data Mining, Pittsburg, PA.
18. Pahl, C., & Donnellan, D. (2002). Data mining technology for the evaluation of web-based teaching and learning systems. In M. Driscoll & T. Reeves (Eds.), Proceedings of the E-Learn 2002 World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 747-752). Norfolk, VA: Association for the Advancement of Computing in Education.
19. Perrault, A. M. (2007). An exploratory study of biology teachers' online information seeking practices. School Library Media Research, 10. Retrieved from http://www.ala.org/ala/mgrps/divs/aasl/aaslpubsandjournals/slmrb/slmrcontents/ volume10/perrault_biologyteachers.cfm
20. Recker, M. (2006). Perspectives on teachers as digital library users: Consumers, contributors, and designers. D-Lib Magazine, 9(3). Retrieved from http://www.dlib.org/dlib/september06/recker/09recker.html
21. Recker, M., Walker, A., Giersch, S., Mao, X., Palmer, B., Johnson, D., Robertshaw, B. (2007). A study of teachers' use of online learning resources to design classroom activities. New Review of Hypermedia and Multimedia, 13(2), 117-134.
22. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. doi: 10.1016/j.eswa.2006.04.005
23. Shreeves, S. L., & Kirkham, C. M. (2004). Experiences of educators using a portal of aggregated metadata. Journal of Digital Information, 5(3). Retrieved from http://journals.tdl.org/jodi/article/viewArticle/144/142
24. Statistical Innovations. (2005). Tutorial 1: Using Latent GOLD® 4.5 to estimate LC cluster models. Retrieved from http://www.statisticalinnovations.com/products/latentgold_v4.html
25. th ACM/IEEE-CS Joint Conference on Digital Libraries (pp.353-356). New York, NY: ACM. doi: 10.1145/1816123.1816178
26. Tanni, M. (2008). Prospective history teachers' information behaviour in lesson planning. Information Research, 13(4). Retrieved from http://informationr.net/ir/13-4/paper374.html
27. th ASEE/IEEE Frontiers in Education Conference, Savannah, GA.
28. Xu, B., Recker, M., & Hsi, S. (2010). The data deluge: Opportunities for research in educational digital libraries. In C. M. Evans (Ed.), Internet issues: Blogging, the digital divide and digital libraries (pp. 95-116). Hauppauge, NY: Nova Science.
29. Xu, B., & Recker, M. (in press). Understanding teacher users of a digital library service: A clustering approach. Journal of Educational Data Mining, 3(3). Retrieved from http://www.educationaldatamining.org/JEDM/index.php?option=com_ content&view=article&id=69&Itemid=66
30. Zimmerman, D. W. (1994). A note on the influence of outliers on parametric and nonparametric tests. Journal of General Psychology, 121(4), 391-401.