Student Admission Data Analytics for Open and Distance Education in Greece
Abstract
Over the last few decades, distance learning has become very popular, as a result of the many pros it offers, along with its flexibility. The need for a better understanding of the data originating from such educational environments has led to the rise of the Educational Data Mining research field. However, most of the studies so far focus on the analysis of the data being collected during and/or after the distance learning courses. In this paper, we study the demographical data related to student applications for acceptance in distance learning programs offered by the Hellenic Open University, during the decade from 2003 to 2013. Our study aims at analyzing the data, and discovering patterns and knowledge that can be used to help the strategic placement of the university, and the improvement of the experience that offers to its students. Moreover, we attempt to correlate the discovered findings with the social and financial status of the applicants’ environment.
Article Details
- Zitationsvorschlag
-
- Ausgabe
- Bd. 13 Nr. 2 (2017)
- Rubrik
- Μέρος πρώτο / Section 1
Οι συγγραφείς των άρθρων που δημοσιεύονται στο περιοδικό διατηρούν τα δικαιώματα πνευματικής ιδιοκτησίας επί των άρθρων τους, δίνοντας στο περιοδικό το δικαίωμα της πρώτης δημοσίευσης. Άρθρα που δημοσιεύονται στο περιοδικό διατίθενται με άδεια Creative Commons 4.0 και σύμφωνα με την άδεια μπορούν να χρησιμοποιούνται ελεύθερα, με αναφορά στο/στη συγγραφέα και στην πρώτη δημοσίευση για μη κερδοσκοπικούς σκοπούς και με δικαίωμα τροποποίησης μόνον με παρόμοια διανομή (αν αναμείξετε, τροποποιήσετε, ή δημιουργήσετε πάνω στο υλικό, πρέπει να διανείμετε τις δικές σας συνεισφορές υπό την ίδια άδεια όπως και το πρωτότυπο).
Downloads
Keine Nutzungsdaten vorhanden.
Literaturhinweise
Baker, R. S. (2010). Data mining for education. International Encyclopedia of Education (3rd edition), 7, 112–118.
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19 (1–2), 205–220.
Bozkurt, A., Akgun-Ozbek, E., Onrat-Yilmazer, S., Erdogdu , E., Ucar, H., Guler, E., Sezgin, S., Karadeniz, A., Sen, N., Goksel-Canbek, N., Dincer, G. D., Ari, S., & Aydin, C. H. (2015). Trends in distance education research: A content analysis of journals 2009-2013. International Review of Research in Open and Distributed Learning, 16 (1), 330–363.
Byrne, T. C. (1989). The evolution of distance education. Calgary, Alberta: University of Calgary Press, 3 (2), 135.
Duffy, T., Gilbert, I., Kennedy, D., & Kwong, P. W. (2002). Comparing distance education and conventional education: Observations from a comparative study of post-registration nurses. Association for Learning Technology Journal, 10 (1), 70–82.
Ehrenberg, R. G., & Sherman, D. R. (1987). Employment while in college, academic achievement, and post-college outcomes: A summary of results. The Journal of Human Resources, 22 (1), 1–23.
European Statistical System. Census Hub. (2011). https://ec.europa.eu/CensusHub2/query.do?step=selectHyperCube&qhc=false
Hämäläinen, W., & Vinni, M. (2010). Classifiers for educational data mining. Handbook of Educational Data Mining Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 57-74.
Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Open University of Cyprus. (2002). Retrieved from http://www.ouc.ac.cy/
Open University of Cyprus. (2002). History of the Open University of Cyprus. Retrieved from http://www.ouc.ac.cy/web/guest/university/genika/history
Orszag, J. M., Orszag, P. R., & Whitmore, D. M. (2001). Learning and earning: Working in college. Upromise.
Pal S. (2012). Educational data mining and learning analytics: Applications to constructionist research. International Journal of Information Engineering and Electronic Business (IJIEEB), 4 (2), 1–7.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).
Pierrakeas, C., Xenos, M., Panagiotakopoulos, C., & Vergidis, D. (2004). A comparative study of dropout rates and causes for two different distance education courses. International Review of Research in Open and Distance Learning (IRRODL), 5 (2), 117–131.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 40 (6), 601–618.
Rossum, G. (1995). Python reference manual.
Smeding, A. (2012). Women in science, technology, engineering, and mathematics (stem): An investigation of their implicit gender stereotypes and stereotypes’ connectedness to math performance. Sex Roles, 67 (11-12), 617–629. doi: http://dx.doi.org/10.1007/s11199-012-0209-4
Smyth, F. L., & Nosek, B. A. (2015). On the gender-science stereotypes held by scientists: explicit accord with gender-ratios, implicit accord with scientific identity. Frontiers in Psychology, 6.
Tiene D. (2000). Online discussion: A survey of advantages and disadvantages compared to face-to-face discussions. Journal of Educational Multimedia and Hypermedia, 9 (4), 371 384.
Turney, P. (2002). Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. Proceedings of the Association for Computational Linguistics, 417 424.
Wen, M., Yang, D., & Rosé, C. P. (2014). Sentiment analysis in MOOC discussion forums: What does it tell us? Educational Data Mining.
White, M. (1982). Distance education in Australian higher education a history. Distance Education, 3 (2), 255–278.
