Mineria de dades educatives i anàlisi de dades de l’aprenentatge: diferències, semblances i evolució en el temps

Laura Calvet Liñán, Ángel Alejandro Juan Pérez

Resum


El progrés tecnològic de les darreres dècades ha fet possible una diversitat de formes d’aprenentatge. Avui dia les universitats ofereixen múltiples models d’ensenyament entre els quals podem triar, per exemple, l’aprenentatge mixt (b-learning) o l’aprenentatge electrònic. Si bé cada cop són més nombroses les oportunitats per a alumnes i professors, l’aprenentatge en línia també planteja dificultats degudes a la manca de contacte humà directe. Els entorns en línia permeten que es generin grans quantitats de dades relacionades amb els processos d’ensenyament i aprenentatge, de les quals es pot extreure una valuosa informació que es pot fer servir per millorar l’actuació de l’alumnat. En aquest treball volem estudiar les semblances i diferències entre la mineria de dades educatives i l’anàlisi de dades sobre l’aprenentatge, dos camps de recerca relativament nous i creixentment populars relacionats amb la recollida, l’anàlisi i la interpretació de dades sobre educació. En tractarem l’origen, els objectius, les diferències i semblances, l’evolució que han tingut en el temps i els reptes a què s’enfronten, així com la seva relació amb les dades massives i els cursos en línia oberts i massius (MOOC).

Paraules clau


aprenentatge en línia, mineria de dades educatives, anàlisi de dades sobre aprenentatge, dades massives

Referències


Akçapinar, G., Coşgun, E., & Altun, A. (2013). Mining Wiki Usage Data for Predicting Final Grades of Students. Proceedings of the International Academic Conference on Education, Teaching and E-learning. ISBN: 978-80-905442-1-5.

Antonenko, P.D., Toy, S., & Niederhauser, D.S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398. doi: 10.1007/s11423-012-9235-8

Baker, R.S.J.D, Corbett, A.T., & Wagner, A.Z. (2006). Human classification of low-fidelity replays of student actions. In M. Ikeda, K. Ashlay, & T. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 29-35). Jhongli, Taiwan: Springer.

Baker, R., Barnes, T., & Beck, J.E. (2008). Proceedings of the 1st International Conference on Educational Data Mining. Montreal, Quebec, Canada.

Baker, R.S.J.D., Costa, E., Amorim, L., Magalhães, J., & Marinho, T. (2012). Mineração de Dados Educacionais: Conceitos, Técnicas, Ferramentas e Aplicações. Jornada de Atualização em Informática na Educação, 1, 1- 29.

Baker, R.S.J.D., & Inventado, P.S. (2014). Educational Data Mining and Learning Analytics. In J.A. Larusson, & B. White (Eds.), Learning Analytics: from Research to Practice (pp. 61-75). NY, USA: Springer.

Baker, R.S.J.D., & Yacef, K. (2009). The State of Educational Data Mining in 2009: A review and future visions. Journal of educational Data Mining, 1, 3- 17.

Barnes, T., Desmarais, M., Romero, C., & Ventura, S. (2009). Proceedings of the 2nd International Conference on Educational Data Mining, Cordoba, Spain.

Berry, M.W., & Kogan, J. (2010). Text mining: applications and theory. Wiley. ISBN: 978-0-470-74982-1

Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. Retrieved from the U.S. Department of Education website: http://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf

Corbett, A.T., & Anderson, J.R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. doi: 10.1007/BF01099821

Daradoumis, T., Juan, A., Lera-López, F., & Faulin, J. (2010). Using Collaboration Strategies to Support the Monitoring of Online Collaborative Learning Activity. In M. Lytras, P. O. D. Pablos, D. Avison, J. Sipior, Q. Jin, W. Leal, L. Uden, M. Thomas, S. Cervai, & D. Horner (Eds.), Technology Enhanced Learning. Quality of Teaching and Educational Reform (pp. 271-277). Springer Berlin Heidelberg.

Daradoumis, T., Rodríguez-Ardura, I., Faulin, J., & Martínez-López, F.J. (2010). CRM Applied to Higher Education: Developing an e-Monitoring System to Improve Relationships in e-Learning Environments. International Journal of Services Technology and Management, 14(1), 103-125. doi: 10.1504/IJSTM.2010.032887

Desmarais, M.C. (2011). Mapping question items to skills with non-negative matrix factorization. SIGKDD Exploration Newsletter, 13 (2), 30-36. doi: 10.1145/2207243.2207248

Dogan, B., & Camurcu, A.Y. (2009). Visual Clustering of Multidimensional Educational Data From an Intelligent Tutoring System. Computer Applications in Engineering Education, 18(2), 375-382. doi: 10.1002/cae.20272

Feidakis, M., Daradoumis, T., Caballé, S., & Conesa, J. (2014). Embedding emotion awareness into e-learning environments. International Journal of Emerging Technologies in Learning, 9(7), 39-46. doi: 10.3991/ijet.v9i7.3727

García, E., Romero, C., Ventura, S., & Castro, C. (2011). A collaborative educational association rule mining tool. Internet and Higher Education, 14, 77-88. doi: 10.1016/j.iheduc.2010.07.006

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2th Edition). Springer. ISBN: 978-0-387-84858-7

Jeong, H., & Biswas, G. (2008). Mining student behavior models in Learning-by-teaching environments. In R. de Baker, T. Barnes, & J. Beck (Eds), Proceedings of the 1st International Conference on Educational Data Mining (pp. 127–136).

Johnson, L., Adams, S., & Cummins, M. (2012). The NMC Horizon Report: 2012 Higher Education Edition. Austin, Texas: The New Media Consortium.

Juan, A., Daradoumis, T., Faulin, J., & Xhafa, F. (2009). A data analysis model based on control charts to monitor online learning processes. Int. J. Business Intelligence and Data Mining, 2(4), 159-174. doi: 10.1504/IJBIDM.2009.026906

Juan, A., Daradoumis, T., Faulin, J., & Xhafa, F. (2009). SAMOS: a model for monitoring students’ and groups’ activities in collaborative e-learning. Int. J. Learning Technology, 4(1/2), 53-72. doi: 10.1504/IJLT.2009.024716

Kim, J.H., Park, Y., Song, J., & Jo, I.-H. (2014). Predicting Students’ Learning Performance by Using Online Behavior Patterns in Blended Learning Environments: Comparison of Two Cases on Linear and Non-linear Model. In J. Stamper, Z. Pardos, M. Mavrikis, B.M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 407-408). London, UK.

Kinnebrew, J., & Biswas, G. (2012). Identifying learning behaviours by contextualizing differential sequence mining with action features and performance evolution. In K. Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 57-64). Chania, Greece.

Kotsiantis, S.B., & Pintelas, P.E. (2005). Predicting students marks in Hellenic Open University. In P. Goodyear, D.G. Sampson, D.J.-T. Yang, Kinshuk, T. Okamoto, R. Hartley, & N.-S. Chen (Eds.), Proceedings of the 5th IEEE International Conference on Advanced Learning Technologies (pp. 664-668). doi: 10.1109/ICALT.2005.223

Kovačić, Z.J. (2012). Predicting student success by mining enrolment data. Research in Higher Education Journal, 15.

Lantz, B. (2013). Machine Learning with R. Birmingham, England: Packt. ISBN: 9781782162148

Larusson, J.A., & White, B. (Eds.) (2014). Learning Analytics: from Research to Practice. NY, USA: Springer. doi: 10.1007/978-1-4614-3305-7

Lee, J.I. & Brunskill, E. (2012). The impact on individualizing student models on necessary practice opportunities. In K. Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 118-125). Chania, Greece.

Long, P., Siemens, G., Conole, G., & Gašević, D. (2011). Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, Canada.

Marquès, J.M., Lazaro, D., Juan, A., Vilajosana, X., Domingo, M., & Jorba, J. (2013). PlanetLab@UOC: A Real Lab Over the Internet to Experiment With Distributed Systems. Computer Applications in Engineering Education, 21(2), 265-275. doi: 10.1002/cae.20468

Moucary, C.E., Khair, M., & Zakhem, W. (2011). Improving Student’s Performance Using Data Clustering and Neural Networks in Foreign-Language Based Higher Education. The Research Bulletin of Jordan ACM, 2(3), 27-34.

Mühlenbrock, M. (2005). Automatic action analysis in an interactive learning environment. Proceedings of the 12 th International Conference on Artificial Intelligence in Education (pp. 73-80). Amsterdam, The Netherlands.

Oeda, S., Ito, Y., & Yamanishi, K. (2014). Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations. In J. Stamper, Z. Pardos, M. Mavrikis, B.M. McLaren (Eds.). Proceedings of the 7th International Conference on Educational Data Mining (pp. 367-368). London, UK.

Palazuelos, C., García-Saiz, D., & Zorrilla, M. (2013). Social Network Analysis and Data Mining: An Application to the E-learning Context. In C. Badica, N.T. Nguyen, M. Brezovan (Eds.), Proceedings of the 5th International Conference on Computational Collective Intelligence (pp. 651-660). Craiova, Romania.

Peña-Ayala, A. (2014). Educational Data Mining: Applications and Trends. NY, USA: Springer.

Romero, C., & Ventura, S. (2006). Data Mining in E-learning. Southampton, UK: Wit-Press.

Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135-146. doi: 10.1016/j.eswa.2006.04.005

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. doi: 10.1109/TSMCC.2010.2053532

Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining Knowl Discov, 3, 12-27. doi: 10.1002/widm.1075

Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R.S.J.D. (Eds.) (2010). Handbook of Educational Data Mining. Boca Ratón, FL: CRC Press.

Siemens, G., & Baker, R.S.J.D. (2012). Learning analytics and educational data mining: towards communication and collaboration. In S.B. Shum, D. Gasevic, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252-254). ACM, NY, USA. doi: 10.1145/2330601.2330661

Tane, J., Schmitz, C., & Stumme, G. (2004). Semantic resource management for the web: an e-learning application. Proceedings of the 13th International Conference of the WWW (pp. 1-10). NY, USA. doi: 10.1145/1013367.1013369.

Trćka, N., Pechenizkiy, M., & Aalst, W.V.D. (2011). Process mining from educational data. In C. Romero, S. Ventura, M. Pechenizkiy, & R.S.J.D. Baker (Eds.). Handbook of Educational Data Mining, (pp. 123-142). Boca Raton, Florida: CRC Press.

Ueno, M. (2004). Online Outlier Detection System for Learning Time Data in E-Learning and Its Evaluation. Proceedings of the International Conference on Computers and Advanced Technology in Education (pp. 248-253). Kauai, Hawaii, USA.

Yadav, S.K., & Pal, S. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal, 2(2), 51-56.




DOI: http://dx.doi.org/10.7238/rusc.v12i3.2515

Enllaços refback

  • No hi ha cap enllaç refback.


Bookmark and Share


Universitat Oberta de Catalunya. eLearn Center 

RUSC. Universities and Knowledge Society Journal és una publicació electrònica editada per la Universitat Oberta de Catalunya (Barcelona).

Creative Commons
Els textos publicats en aquesta revista estan subjectes –llevat que s'indiqui el contrari– a una llicència de Reconeixement 3.0 Espanya de Creative Commons. Podeu copiar-los, distribuir-los, comunicar-los públicament i fer-ne obres derivades sempre que reconegueu els crèdits de les obres (autoria, nom de la revista, institució editora) de la manera especificada pels autors o per la revista. La llicència completa es pot consultar a http://creativecommons.org/licenses/by/3.0/es/deed.ca.