Education data mining with Moodle 2.4
Abstract
The goal of e-learning environments is to supply effective learning methods, to enable the users to approach certain resources at any time, to set solutions for certain problems, assessment for the work etc. One of the best known environments of this kind is e-learning system Moodle. These environments like Moodle use and save large amount of data in their databases, but in most cases they don't offer enough information of the course participants and their activities in the system. The aim of this work is, by the use of data mining techniques such as classification, clustering, statistics and regression, to describe the process of selection and acquiring data from the Moodle database, and to create dashboard - web based application, that would communicate with the e-learning system Moodle and supply multilevel approach as: manager, administrator, teacher and user level; and practically will improve the approach to evaluation of larger groups of participants in the learning process. This will help teachers to evaluate web activity of the students, to get more objective feedback and find out more about how the students learn. Also this dasboard will directly solve the teachers problems in the terms of dealing with this kind of platforms and big amounts of data.References
Ramaswami M., and Bhaskaran R.: A Study on Feature Selection Techniques in Educational Data Mining”, vol.1, Journal Of Computing, ISSN:2151-9617, https://sites.google.com/site/journalofcomputing (December 2009)
Elatia S., Ipperciel D., Hammad A.:Implications and Challenges to Using Data Mining in Educational Research in the Canadian Context, Canadian journal Of Education, pp. 101--119 (2012)
Baradwaj B. K., Pal S.:Mining Educational Data to Analyze Students’ Performance, (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 2, no. 6 (2011)
Yadav S. K., Bharadwaj B., Pal S.:Mining Education Data to Predict Student’s Retention - A comparative Study, (IJCSIS) International Journal of Computer Science and Information Security, vol. 10, no. 2 (2012)
Cocea M., Weibelzahl S.: Disengagement Detection in Online Learning - Validation Studies and Perspectives, IEEE transactions on learnin technologies, vol. 4, no. 2 (April-June 2011)
Romero C., Ventura S., García E.:Data mining in course management systems - Moodle case study and tutorial
BAKER R.S.J.D., YACEF K.:The State of Educational Data Mining in 2009 - Review and Future Visions
Retalis S., Papasalouros A., Psaromiligkos Y., Siscos S., Kargidis T.: Towards Networked Learning Analytics – A concept and a tool
Romero C., Ventura S., Espejo P. G. and Hervás C.: Data Mining Algorithms to Classify Students, The 1st International Conference on Educational Data Mining, Montréal, Québec, Canada, pp. 8-18 (June 20-21, 2008)
Yadav S. K., Pal S.: Data Mining - A Prediction for Performance Improvement of Engineering Students using Classification, World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 vol. 2, no. 2, pp. 51-56 (2012)
Dash M., Liu H.:Feature Selection for Classification, An International Journal of Intelligent Data Analysis, vol. 1, no. 3,2006, pp. 131-156 (1997)
Chen G., Liu C., Ou K., Liu B.: Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology, Journal of Educational Computing Research, pp. 305–332 (2000)
Anozie N., Junker B.W.: Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system, Educational Data Mining AAAI Workshop, pp. 1-6, California, USA (2006)
Moodle org. LMS Moodle official site. web. 11 Apr. 2013, http://moodle.org
High school Dobri Daskalov, E-learning Moodle, Kavadarci, R. of Macedonia: n.p., 2009. web. 11 Apr. 2013, http://moodle.dobridaskalov.edu.mk
Darrell M. W.:Big Data for Education - Data Mining, Data Analytics, and Web Dashboards, U.S. Department of Education Office of Educational Technology, Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics”, pp. 36 (2012)
Oracle. Oracle Data Mining Concepts. Release 1 (11.1), Oracle Data Mining Concepts , web. 7 Sept. 2013