Mineração de Dados Educacionais para Aperfeiçoamento da Gestão do E-learning: uma Revisão Sistemática de Literatura
DOI:
https://doi.org/10.21714/2238-104X2020v10i2-48085Resumo
Objetivo: Uma vez que a mineração de dados educacionais auxilia na análise da grande quantidade de dados que sistemas de e-learning geram, aperfeiçoando os sistemas de ensino-aprendizagem, o presente estudo procura, por meio de uma revisão sistemática de literatura, verificar como o tema está sendo abordados nas pesquisas do campo nos últimos anos. Metodologia: Dado que tal contexto é emergente, novo e dinâmico e que necessita de análises de sua estrutura para entender suas características, dificuldades e potencialidades, este estudo foi desenvolvido para analisar as estruturas existentes de sistemas de e-learning, assim como identificar como o processo de mineração de dados educacionais pode ser aplicado no ensino a distância, por meio de uma revisão sistemática de literatura. Resultados: Os resultados estão divididos em duas partes: (i) análise descritiva, em que os artigos são apresentados a partir de uma análise bibliométrica, e (ii) análise semântica, em que os artigos são analisados a partir do modelo de quatro etapas do processo de mineração de dados educacionais. Contribuições: Os resultados contribuem com recomendações práticas e acadêmicas, pois apresentam tanto como o processo de mineração de dados interfere na gestão do e-learning, bem como um panorama das pesquisas existentes, a fim de conhecer a pesquisa atual da temática analisada.
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Referências
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.
