Abstract
In this research we applied classification models for prediction of students’ performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.
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Y-C Lee, N. Terashima, A Distance Instructional System with Learning Performance Evaluation Mechanism: Moodle-Based Educational System Design, Distance Education Technologies 10 (2) (2012). doi: 10.4018/jdet.2012040104
T. Martin-Blas, A. Serano-Fernandez, The role of new technologies in the learning process: Moodle as a teaching tool in Physics, Computers & Education 52 (2009) pp. 35–44. doi:10.1016/j.compedu.2008.06.005
I. Kazanidis, S. Valsamidis, T. Theodosiou and S. Kontogiannis, Proposed framework for data mining in e-learning: The case of Open e-Class, in Proc. IADIS International Conference of Applied Computing, (Rome, Italy, 2009), pp. 254–258.
F. J. García-Peñalvo, M. Á.Conde, M. Alier, María J. Casany, Opening Learning Management Systems to Personal Learning Environments, Journal of Universal Computer Science 17(9)(2011), pp. 1222–1240.
A. J. Berlanga, F. J. García-Peñalvo, P. B. Sloep, Towards eLearning 2.0 University. Interactive Learning Environments 18 (3) (2010), pp. 199–201.
C. Romero, S. Ventura and E. García, Data mining in course management systems: moodle case study and tutorial, Comput. Educ. 51(1) (2008) 368–384.
V. Kumar, An Empirical Study of the Applications of Data Mining Techniques in Higher Education, International Journal of Advanced Computer Science and Applications, 2(3) (2011) 80–84.
V.Ramesh, P.Parkavi, P.Yasodha, Performance Analysis of Data Mining Techniques for Placement Chance Prediction, International Journal of Scientific & Engineering Research 2 (8) (2011) pp. 1–7.
F. Castro, A. Vellido, À. Nebot and F. Mugica, Applying data mining techniques to e-learning problems. Evolution of teaching and learning paradigms in intelligent environment, 62 (2007) pp. 183–221.
A. C. Romero, and A. S. Ventura, Educational data mining: A survey from 1995 to 2005, Journal of Expert Systems Applications, 33(1) (2007) 135–146.
C-H Weng, Mining fuzzy specific rare itemsets for education data, Knowledge-Based Systems 24 (5) (2011) pp. 697–708.
C. Romero and S. Ventura, Educational data mining: a review of the state-of-the-art, IEEE Trans. Syst. Man Cybernet. C Appl. Rev. , 40(6) (2011) 601–618.
A. Krueger, A. Merceron and B. Wolf, A Data Model to Ease Analysis and Mining of Educational Data, in Proc. Third International Conference on Educational Data Mining, (USA, Pittsburgh, 2010) pp. 131–140.
Y-H Wang, H-C Liao, Data mining for adaptive learning in a TESL-based e-learning, Expert Systems with Applications 38 (6) (2011), pp. 6480–6485.
V.Ramesh, P.Parkavi, P.Yasodha, Performance Analysis of Data Mining Techniques for Placement Chance Prediction, International Journal of Scientific & Engineering Research 2 (8) (2011).
C. Vialardi, J. Chue, J.P. Peche, G. Alvarado, B. Vinatea, J. Estrella and Á. Ortigosa, A data mining approach to guide students through the enrollment process based on academic performance, User modeling and user-adapted interaction 21 (1–2) (2011), pp. 217–248. doi: 10.1007/s11257-011-9098-4.
S. Kotsiantis, K. Patriarcheas and M. Xenos, A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education, Knowledge-Based Systems, 23(6) (2010) 529–535.
K. Kuk, P. Spalevic, S. Ilic, M. Caric, Z. Trajcevski, A Model for Student Knowledge Diagnosis through Game Learning Environment, Technics Technologies Education Management – TTEM, 7 (1) (2012) 103–110.
S. Kotsiantis, Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades, Artificial Intelligence Review, (Online First) (2011) 1–14.
N. Myller, J. Suhonen and E. Sutinen, Using Data Mining for Improving Web-Based Course Design, in Proc. International Conference on Computers in Education, (USA, Washington, 2002) pp. 959–964.
D. Traynor and J.P. Gibson, Synthesis and Analysis of Automatic Assessment Methods in CS1, in Proc. The 36th SIGCSE Technical Symposium on Computer Science Education SIGCSE’05, (ACM Press., Louis Missouri, USA , 2005) pp. 495–499.
B. Minaei-bidgoli, D. A. Kashy, G. Kortmeyer and W. F. Punch, Predicting student performance: an application of data mining methods with an educational Web-based system, in Proc. 33rd International Conference on Frontiers in Education, (Colorado, Westminister, 2003) pp. 13–18.
M. Delgado, E. Gibaja, M.C. Pegalajar and O. Pérez, (2006). Predicting Students’ Marks from. Moodle Logs using Neural Network Models, in Proc. International Conference on Current Developments in Technology Assisted Education, (Sevilla, Spain, 2006) pp. 586–590.
F.A. Bachtiar, W.E. Cooper, K.K. Kamei, Student grouping by neural network based on affective factors in learning English in Proc. International Conference on e-Education, Entertainment and e-Management (ICEEE), 2011. doi: 10.1109/ICeEEM.2011.6137792.
A. Drigas, J. Vrettaros, An Intelligent Tool for Building e-Learning Contend-Material Using Natural Language in Digital Libraries. WSEAS Transactions on Information Science and Applications 5(1) (2004) 1197–1205.
K. Hammouda, M. Kamel, Data Mining in e-Learning. In: Pierre, S. (ed.): e-Learning Networked Environments and Architectures: A Knowledge Processing Perspective. Springer-Verlag, Berlin Heidelberg New York (2005).
J. Tane, C. Schmitz, G. Stumme, Semantic Resource Management for the Web: An e-Learning Application. In: Fieldman, S., Uretsky, M. (eds.): The 13th World Wide Web Conference 2004, WWW2004. ACM Press, New York (2004) pp. 1–10
S. Ayesha, T. Mustafa, A.R. Sattar, M.I. Khan, Data Mining Model for Higher Education System, Europen Journal of Scientific Research 43(1)(2010), pp.24–29.
E. Ayers, R. Nugent, and N. Dean, A Comparison of Student Skill Knowledge Estimates. in Proc. International Conference On Educational Data Mining, (Cordoba, Spain, 2009), pp. 1–10.
D. Zakrzewska, Cluster analysis for user’s modeling in intelligent e-learning systems, in Proc. In International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (IEA/AIE ‘08, eds. N. T. Nguyen, L. Borzemski, A.Grzech, and M. Ali, (Springer-Verlag, Berlin, Heidelberg, 2008) pp. 209–214.
R.M. Felder and L.K. Silverman, Learning and teaching styles in engineering education, Eng. Educ. , 78 (7) (1988) 674–681.
A. Heikkila, N. Markku, J. Nieminen, K. Lonka, Interrelations among university students’ approaches to learning, regulation of learning, and cognitive and attributional strategies: a person oriented approach, High Educ 61 (2011), pp. 513–529. doi: 10.1007/s10734-010-9346-2
D. Perera, J. Kay, I. Koprinska, K. Yacef and O. R. Zaïane, Clustering and Sequential Pattern Mining of Online Collaborative Learning Data, IEEE Transaction on Knowledge and Data Engineering, 21 (6) (2009), pp. 759–772.
S.Y. Chen and X. Liu, Mining students’ learning patterns and performance in Web-based instruction: a cognitive style approach, Interactive Learning Environments 19 (2) (2011). doi:10.1080/10494820802667256
J.M. Adán-Coello C.M. Tobar E.S.J. de Faria, W.S de Menezes, R.L. de Freitas, Forming Groups for Collaborative Learning of Introductory Computer Programming Based on Students’ Programming Skills and Learning Styles, International Journal of Information and Communication Technology Education 7 (4) (2011). doi: 10.4018/jicte.2011100104
S. Jevremovic, Implementation of the adaptive system in electronic learning, Management 14 (53) (2009), pp.57–61.
C-M. Chen, M-C. Chen, Mobile formative assessment tool based on data mining techniques for supporting web-based learning, Computers & Education 52 (2009), pp. 256–273. doi:10.1016/j.compedu.2008.08.005
I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz and T. Euler, YALE: Rapid prototyping for complex data mining tasks, in Proc. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (Philadelphia, USA, ACM Press, 2006) pp. 935–940.
J. Cardoso, Developing Course Management Systems Using The Semantic Web, The Semantic Web, Semantic Web and Beyond, 2008, Volume 6, Part IV, 169–188. doi: 10.1007/978-0-387-48531-7_8
M. Minović, M. Milovanović, I. Kovačević, J. Minović and D. Starčević, Game design as a learning tool for the course of Computer Networks, International Journal of Engineering Education, 27(3) (2011) 498–508.
I.B. Myers and M.H. McCaulley, Manual: a guide to the development and use of the Myers±Briggs Type Indicator, (Consulting Psychologists Press, Palo Alto, CA, 1985).
I.B. Myers, M.H. McCaulley, N.L. Quenk and A.L. Hammer, MBTI Manual. A guide to the Development and Use of Myers-Briggs Type Indicator, (Consulting Psychologists Press, Palo Alto, CA, 1998).
C.G. Jung, Psychological Types. The Collected Works, vol. 6. , (Routledge and Kegan Paul, London, UK, 1971)
J.G. Carlson, Resent Assessments of Myers-Briggs Type Indicator, Journal of Personality Assessment, 49(4) (1985) 356–365.
R. Colomo Palacios, E. Tovar Caro, A. García Crespo, & J.M. Gómez Berbís, Identifying Technical Competences of IT Professionals: The Case of Software Engineers, International Journal of Human Capital and Information Technology Professionals 1(1) (2010), pp. 31–43.
R. Colomo-Palacios, E. Fernandes, P. Soto-Acosta & M. Sabbagh, M, Software product evolution for Intellectual Capital Management: The case of Meta4 PeopleNet, International Journal of Information Management 31(4) (2011), pp. 395–399.
A. García-Crespo, R. Colomo-Palacios, J.M Gómez-Berbís, & M. Mencke, M,. BMR: Benchmarking Metrics Recommender for Personnel issues in Software Development Projects. International Journal of Computational Intelligence Systems 2(3) (2009), pp. 257–267.
S. Westlund, Leading Techies: Assessing Project Leadership Styles Most Significantly Related to Software Developer Job Satisfaction. International Journal of Human Capital and Information Technology Professionals 2(2) (2011), pp. 1–15. doi:10.4018/jhcitp.2011040101
O.C.S. Tzeng, S.L. Ware, J-M. Chen, Measurement and Utility of Continuous Unipolar Ratings for the Myer-Briggs Type Indicator, Journal of Personality Assessment, 53(4) (1989) 727–738.
C. Romero, S. Ventura, P. G. Espejo and C. Hervs, Data Mining Algorithms to Classify Students, in Proc. 1st International Conference on Educational Data Mining (EDM’08), (Montreal, Canada, 2008) pp. 8–17.
P. Lingras, M. Joshi, Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering, International Journal of Computational Intelligence Systems, 4(1)(2011), pp.12–28.
Y.-C. Lin, T.-K. Wu, S.-C. Huang, Y.-R. Meng, W.-Y. Liang, Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities, International Journal of Computational Intelligence Systems, 4(1) (2011), pp.29–43.
M. Matijaš, M. Vukićević, S. Krajcar, Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input, Journal of Electrical Engineering 62(5)(2011) pp. 280–285. doi:10.2478/v10187-011-0044-9
B. Delibašić, M. Jovanović, M. Vukićević, M. Suknović, Z. Obradović, Component-based decision trees for classification, Intelligent Data Analysis 15 (5) (2011) pp. 671–693. doi: 10.3233/IDA-2011-0489
M. Suknovic, B. Delibasic, M. Jovanovic, M. Vukicevic, D. Becajski-Vujaklija and Z. Obradovic, Reusable components in decision trees induction algorithms, Computational Statistics (2012). doi:10.1007/s00180-011-0242-8.
B. Delibasic, K. Kirchner, J. Ruhland, M. Jovanovic, M. Vukicevic, Reusable components for partitioning clustering algorithms. Artificial Intelligence Review 32 (1–4) (2009) pp. 59–75. doi: 10.1007/s10462-009-9133-6
M. Vukicevic, M. Jovanovic, B. Delibasic, S. Isljamovic, M. Suknovic, Reusable component-based architecture for decision tree algorithm design, International Journal on Artificial Intelligence Tools (2012). doi: 10.1142/S0218213012500224
B. Delibasic, M. Vukicevic, M. Jovanovic, K. Kirchner, J. Ruhland, M. Suknovic, An architecture for component-based design of representative-based clustering algorithms, Data & Knowledge Engineering (2012). doi: 10.1016/j.datak.2012.03.005
B. Chen and T. Bryer, Investigating Instructional Strategies for Using Social Media in Formal and Informal Learning, The International Review of Research in Open and Distance Learning, ISSN: 1492-3831, 13 (1) (2012).
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Jovanovic, M., Vukicevic, M., Milovanovic, M. et al. Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study. Int J Comput Intell Syst 5, 597–610 (2012). https://doi.org/10.1080/18756891.2012.696923
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DOI: https://doi.org/10.1080/18756891.2012.696923