Bresfelean, Vasile Paul (2008): Data Mining Applications in Higher Education and Academic Intelligence Management. Published in: Theory and Novel Applications of Machine Learning (10. January 2009): pp. 209-228.
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Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.
|Item Type:||MPRA Paper|
|Original Title:||Data Mining Applications in Higher Education and Academic Intelligence Management|
|English Title:||Data Mining Applications in Higher Education and Academic Intelligence Management|
|Keywords:||data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management|
|Subjects:||A - General Economics and Teaching > A2 - Economics Education and Teaching of Economics
C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology; Computer Programs
|Depositing User:||Vasile Paul Bresfelean|
|Date Deposited:||13. Mar 2010 10:55|
|Last Modified:||12. Feb 2013 04:33|
Antunes C., Acquiring Background Knowledge for Intelligent Tutoring Systems, Proceedings of Educational Data Mining 2008, The 1st International Conference on Educational Data Mining Montreal, Quebec, Canada, June 20-21, 2008 pp.18-27
Anjewierden A., Kollöffel B., Hulshof C., Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. ADML 2007, Crete, September 2007. pp. 27-36.
Bohanec, M., Zupan, B., Integrating decision support and data mining by hierarchical multiattribute decision models, IDDM-2001: ECML/PKDD-2001 Workshop Integrating Aspects of Data Mining, Decision Support and Meta-Learning, Freiburg, 2001, pp. 25-36.
Bresfelean, V.P., Bresfelean, M., Ghisoiu, N., Comes, C.-A., Determining Students’ Academic Failure Profile Founded on Data Mining Methods, 30th International Conference Information Technology Interfaces, ITI 2008, 23-26 June 2008 Cavtat, Croatia (a)
Bresfelean, V.P., Bresfelean, M., Ghisoiu, N., Comes, C.-A., Development of universities’ management based on data mining researches, INTED 2008, International Technology, Education and Development Conference, March 3-5 2008 Valencia, Spain (b)
Bresfelean V.P., Analysis and predictions on students’ behavior using decision trees in Weka environment, 29th International Conference Information Technology Interfaces, ITI 2007, Cavtat, Croatia, June 2007, pp. 51-56
Bresfelean V.P, Bresfelean M, Ghisoiu N, Comes C-A., Data mining clustering techniques in academia, 9th International Conference on Enterprise Information Systems, 12-16, June 2007, Funchal, Portugal, pp. 407-410
Bresfelean V.P, Bresfelean M, Ghisoiu N, Comes C-A., Continuing education in a future EU member, analysis and correlations using clustering techniques, Proceedings of EDU'06 International Conference, Tenerife, Spain, December 2006, pp. 195-200
Bresfelean V.P., Implicaţii ale tehnologiilor informatice asupra managementului institutiilor universitare, Ed. Risoprint, Cluj-Napoca, 2008
Dasgupta S., Long P.M., Performance Guarantees for Hierarchical Clustering, Journal of Computer and System Sciences, Volume 70 , Issue 4, June 2005, Special issue on COLT 2002, pp. 555 – 569
Dustdar, S., Caramba—A Process-Aware Collaboration System Supporting Ad hoc and Collaborative Processes in Virtual Teams, Distributed and Parallel Databases, 15, Kluwer Academic Publishers, 2004
Edelstein H., Introduction to Data Mining and Knowledge Discovery. Third Edition. Two Crows Corporation, Potomac, MD, USA, 1999
Heiner, C., Baker, R., Yacef, K.: Preface. In: Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), Jhongli, Taiwan. 2006
Hung, M. C., Wu, J., Chang, J.H., Yang, D. L., 2005. An Efficient K-Means Clustering Algorithm Using Simple Partitioning. Journal of Information Science and Engineering 21, 1157-1177, 2005
Jung, Y.; Park, H.; Du, D.Z.; Drake, B. (2003) A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering, Journal of Global Optimization 25: 91–111, Kluwer Academic Publishers 2003
Kalathur S. An Object-Oriented Framework for Predicting Student Competency Level in an Incoming Class, Proceedings of SERP'06 Las Vegas , 2006, pp. 179-183
Luan Jing, Data Mining Applications in Higher Education, SPSS Exec. Report, 2004. http://www.spss.com/home_page/wp2.htm
Maulik, U., Bandyopadhyay, S., 2002. Performance Evaluation of Some Clustering Algorithms and Validity Indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 12, December 2002
Minaei-Bidgoli B., Punch W.F., Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System, GECCO 2003 Conference, Springer-Verlag, Vol 2, Chicago, USA; July 2003. pp. 2252-2263.
Mostow J., Beck J., Cen H., Cuneo A., Gouvea E., Heiner C. ,An educational data mining tool to browse tutor-student interactions: Time will tell! Proceedings of the Workshop on Educational Data Mining, Pittsburgh, USA; 2005. pp.15-22.
Myller N., Suhonen J, Sutinen E. Using data mining for improving web-based course design, Proceedings ICCE’02 of the International Conference on Computers in Education, Auckland, New Zealand vol.2; December, 2002. pp.959 – 963.
Pimentel E.P., Omar N., Towards a model for organizing and measuring knowledge upgrade in education with data mining, The 2005 IEEE International Conference on Information Reuse and Integration, Las Vegas, USA; August 15-17, 2005. pp. 56-60
Ravi S., Kim J., Shaw E., Mining On-line Discussions: Assessing Technical Quality for Student Scaffolding and Classifying Messages for Participation Profiling,Workshop of Educational Data Mining, Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education. Marina del Rey,CA. USA. July 2007, pp. 70-79
Rodrigues, J.P.C., Barrulas, M. J. (2003), Towards Web-Based Information and Knowledge Management in Higher Education Institutions, Lecture Notes in Computer Science,Volume 2720, Sep 2003, pp. 188-197
Romero C., Ventura S., Espejo P. and Hervas C., Data Mining Algorithms to Classify Students, Proceedings of Educational Data Mining 2008, The 1st International Conference on Educational Data Mining Montreal, Quebec, Canada, June 20-21, 2008 pp. 8-17
Rupnik R., Kukar, M., Bajec M., Krisper, M., DMDSS: Data mining based decision support system to integrate data mining and decision support, 28th International Conference Information Technology Interfaces, ITI 2006, Cavtat, Croatia, June 2006,pp.225-230
Rusu, L., Breşfelean, V.P., Management prototype for universities. Annals of the Tiberiu Popoviciu Seminar, Supplement: International Workshop in Collaborative Systems, Volume 4, 2006, Mediamira Science Publisher, Cluj-Napoca, Romania, pp. 287-295
Universitatea Babes-Bolyai Cluj-Napoca, Romania. Programul Strategic al Universitatii Babes-Bolyai (2007-2011), Nr.11.366; 1 august 2006.
Vandamme J.P., Meskens N., Superby J.F., Predicting Academic Performance by Data Mining Methods, Education Economics, Volume 15, Issue 4 December 2007 , pp. 405 – 419
Witten I.H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann series in data management systems, Elsevier Inc., 2005.