Educational Data Mining to Improve E-learning Management: a Systematic Literature Review
DOI:
https://doi.org/10.21714/2238-104X2020v10i2-48085Abstract
Purpose: Since the mining of educational data helps to analyze the large amount of data generated by e-learning systems, improving the teaching-learning systems, the present study seeks, through a systematic literature review, to check how the topic has been, in recent years, researched. Approach: Once this context is new, emerging and dynamic and that needs analysis of its structure to understand its dynamics, difficulties and potential, this study was developed to analyze the existing structures of e-learning systems, as well as to identify how the educational data mining process can be applied in e-learning. Results: The results are divided into two parts: (i) descriptive analysis, in which the articles are presented from a bibliometric analysis, and (ii) semantic analysis, in which the articles are analyzed using the four-step model of the educational data mining process. Contributions: The results contrib-ute with practical and academic recommendations, as they present as much as the process of data min-ing interferes in the management of e-learning, as well as an overview of the existing researches, in order to know the current research of the analyzed theme.
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Acampora, G., Cadenas, J. M., Loia, V., & Ballester, E. M. (2011). A multi-agent memetic system for human-based knowledge selection. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(5), 946-960.
Aher, S. B., & Lobo, L. M. R. J. (2013). Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowledge-Based Systems, 51, 1-14.
Anaya, A. R., & Boticario, J. G. (2011). Application of machine learning techniques to analyse student interactions and improve the collaboration process. Expert Systems with Applications, 38(2), 1171-1181.
Anaya, A. R., Luque, M., & García-Saiz, T. (2013). Recommender system in collaborative learning environment using an influence diagram. Expert Systems with Applications, 40(18), 7193-7202.
Anaya, A. R., Luque, M., & Peinado, M. (2016). A visual recommender tool in a collaborative learning experience. Expert Systems with Applications, 45, 248-259.
Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242.
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194.
Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., & Tsolakidis, A. (2014). Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences, 147, 390-397.
Chamizo-Gonzalez, J., Cano-Montero, E. I., Urquia-Grande, E., & Muñoz-Colomina, C. I. (2015). Educational data mining for improving learning outcomes in teaching accounting within higher education. The International Journal of Information and Learning Technology, 32(5), 272-285.
Chen, C. M., & Chen, M. C. (2009). Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers & Education, 52(1), 256-273.
Cocea, M., & Weibelzahl, S. (2009). Log file analysis for disengagement detection in e-Learning environments. User Modeling and User-Adapted Interaction, 19(4), 341-385.
Cocea, M., & Weibelzahl, S. (2010). Disengagement detection in online learning: Validation studies and perspectives. IEEE transactions on learning technologies, 4(2), 114-124.
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256.
Cuéllar, M. P., Delgado, M., & Pegalajar, M. C. (2011). Improving learning management through semantic web and social networks in e-learning environments. Expert Systems with Applications, 38(4), 4181-4189.
Ćukušić, M., Alfirević, N., Granić, A., & Garača, Ž. (2010). e-Learning process management and the e-learning performance: Results of a European empirical study. Computers & Education, 55(2), 554-565.
Farid, S., Ahmad, R., Alam, M., Akbar, A., & Chang, V. (2018). A sustainable quality assessment model for the information delivery in E-learning systems. Information Discovery and Delivery, 46(1), 1-25.
Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335-343.
Galvão, C. M., Sawada, N. O., & Trevizan, M. A. (2004). Revisão sistemática. Rev Latino-am enfermagem, 12(3), 549-56.
García, E., Romero, C., Ventura, S., & De Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1-2), 99-132.
García, E., Romero, C., Ventura, S., & De Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88.
Gregg, D. G. (2007). E-learning agents. The Learning Organization, 14(4), 300-312.
Haq, A. U., Magoulas, G., Jamal, A., Majeed, A., & Sloan, D. (2018). Users’ perceptions of e-learning environments and services effectiveness: The emergence of the concept functionality model. Journal of Enterprise Information Management, 31(1), 89-111.
Harrati, N., Bouchrika, I., & Mahfouf, Z. (2017). Investigating the uptake of educational systems by academics using the technology to performance chain model. Library Hi Tech, 35(4), 629-648.
Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469-478.
Huang, T. C. K., Huang, C. H., & Chuang, Y. T. (2016). Change discovery of learning performance in dynamic educational environments. Telematics and Informatics, 33(3), 773-792.
Joorabchi, A., English, M., & Mahdi, A. E. (2016). Text mining stackoverflow: An insight into challenges and subject-related difficulties faced by computer science learners. Journal of Enterprise Information Management, 29(2), 255-275.
Kardan, A. A., Aziz, M., & Shahpasand, M. (2015). Adaptive systems: a content analysis on technical side for e-learning environments. Artificial intelligence review, 44(3), 365-391.
Khodabandeh, A., Afshari, H., & Manian, A. (2010, June). Critical factors affecting e-learner’s satisfaction an empirical study. In EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education (AACE).
Köck, M., & Paramythis, A. (2011). Activity sequence modelling and dynamic clustering for personalized e-learning. User Modeling and User-Adapted Interaction, 21(1-2), 51-97.
Kotsiantis, S. B. (2012). Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4), 331-344.
Lamph, G., Sampson, M., Smith, D., Williamson, G., & Guyers, M. (2018). Can an interactive e-learning training package improve the understanding of personality disorder within mental health professionals?. The Journal of Mental Health Training, Education and Practice, 13(2), 124-134.
Lara, J. A., Lizcano, D., Martínez, M. A., Pazos, J., & Riera, T. (2014). A system for knowledge discovery in e-learning environments within the European Higher Education Area–Application to student data from Open University of Madrid, UDIMA. Computers & Education, 72, 23-36.
Lee, C. H., Lee, G. G., & Leu, Y. (2009). Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning. Expert Systems with Applications, 36(2), 1675-1684.
Lee, Y. J. (2017). Modeling students’ problem solving performance in the computer-based mathematics learning environment. The International Journal of Information and Learning Technology, 34(5), 385-395.
Lin, C. F., Yeh, Y. C., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199-210.
Luo, N., Zhang, M., & Qi, D. (2017). Effects of different interactions on students' sense of community in e-learning environment. Computers & Education, 115, 153-160.
Luzcando, D. R., Ramirez, J., & Lobo, M. B. (2017). Predicting student actions in a procedural training environment. IEEE Transactions on Learning Technologies, 10(4), 463-474.
Male, G., & Pattinson, C. (2011). Enhancing the quality of e-learning through mobile technology: A socio-cultural and technology perspective towards quality e-learning applications. Campus-Wide Information Systems, 28(5), 331-344.
Oliveira, P. C. D., Cunha, C. J. C. D. A., & Nakayama, M. K. (2016). Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda. JISTEM-Journal of Information Systems and Technology Management, 13(2), 157-180.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4), 1432-1462.
Preidys, S., & Sakalauskas, L. (2010). Analysis of students’ study activities in virtual learning environments using data mining methods. Technological and economic development of economy, 16(1), 94-108.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
Romero, C., Ventura, S., Zafra, A., & De Bra, P. (2009). Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers & Education, 53(3), 828-840.
Simo, A., Barbulescu, C., & Kilyeni, S. (2015). Current practices in e-learning: A case study for electrical power engineering in higher education. Procedia-Social and Behavioral Sciences, 191, 605-610.
Siqueira, S. W. M., Braz, M. H. L. B., & Melo, R. N. (2007). Modeling e‐learning content. International Journal of Web Information Systems.
Tan, C. J., Lim, T. Y., Bong, C. W., & Liew, T. K. (2017). A multi-objective evolutionary algorithm-based soft computing model for educational data mining: A distance learning experience. Asian Association of Open Universities Journal, 12(1), 106-123.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
Wang, Y. H., & Liao, H. C. (2011). Data mining for adaptive learning in a TESL-based e-learning system. Expert Systems with Applications, 38(6), 6480-6485.
Weiser, O., Blau, I., & Eshet-Alkalai, Y. (2018). How do medium naturalness, teaching-learning interactions and Students' personality traits affect participation in synchronous E-learning?. The Internet and Higher Education, 37, 40-51.
Xie, T., Zheng, Q., Zhang, W., & Qu, H. (2017). Modeling and predicting the active video-viewing time in a large-scale E-learning system. IEEE Access, 5, 11490-11504.
Zafra, A., & Ventura, S. (2012). Multi-instance genetic programming for predicting student performance in web based educational environments. Applied Soft Computing, 12(8), 2693-2706.
