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  • Special Section: Learning Analytics: Intelligent Decision Support Systems for Learning Environments
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Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Minería de datos educativos y análisis de datos sobre aprendizaje: diferencias, parecidos y evolución en el tiempo

Abstract

Technological progress in recent decades has enabled people to learn in different ways. Universities now have more educational models to choose from, i.e., b-learning and e-learning. Despite the increasing opportunities for students and instructors, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students’ performance. In this paper, we aim to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data. Their origins, goals, differences, similarities, time evolution, and challenges are addressed, as are their relationship with Big Data and MOOCs.

Resumen

El progreso tecnológico de las últimas décadas ha hecho posible una diversidad de formas de aprendizaje. Hoy en día las universidades ofrecen múltiples modelos de enseñanza entre los que poder elegir, por ejemplo aprendizaje mixto (b-learning) o aprendizaje electrónico. Aunque cada vez son más numerosas las oportunidades para alumnos y profesores, el aprendizaje en línea también plantea dificultades debidas a la falta de contacto humano directo. Los entornos en linea permiten generar grandes cantidades de datos relacionados con los procesos de enseñanza-aprendizaje, de los que se puede extraer una valiosa información que se puede usar para mejorar el desempeño del alumnado. En este trabajo queremos estudiar los parecidos y diferencias entre la minería de datos educativos y el análisis de datos sobre aprendizaje, dos campos de investigación relativamente nuevos y crecientemente populares relacionados con la recogida, el análisis y la interpretación de datos educativos. Trataremos su origen, objetivos, diferencias y parecidos, evolución en el tiempo y retos a los que se enfrentan, así como su relación con los macrodatos y los cursos en línea abiertos y masivos (MOOC).

References

  1. 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: http://dx.doi.org/10.1007/s11423-012-9235-8

  2. 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.

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

  4. 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.

  5. 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). New York, NY: Springer.

  6. 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.

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

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

  9. Clark, D. (2013). Adaptive MOOCs. Retrieved from http://www.cogbooks.com/white-papers-AdaptiveMOOCs.html

  10. Clow, D. (2013). MOOCs and the funnel of participation. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (pp. 185–189). doi: http://dx.doi.org/10.1145/2460296.2460332

  11. Daradoumis, T., Bassi, R., Xhafa, F., & Caballé, S. (2013). A review on massive e-learning (MOOC) design, delivery and assessment. Proceedings of the 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 208–213). Compiegne, France. doi: http://dx.doi.org/10.1109/3pgcic.2013.37

  12. 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, D. Horner (Eds.), Technology Enhanced Learning. Quality of Teaching and Educational Reform (pp. 271–277). Springer Berlin Heidelberg. doi: http://dx.doi.org/10.1007/978-3-642-13166-0_39

  13. 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: http://dx.doi.org/10.1504/IJSTM.2010.032887

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

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

  16. Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15(3), 42–57.

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

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

  19. Johnson, L., Adams, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). The NMC Horizon Report: 2013 Higher Education Edition. The New Media Consortium.

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

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

  22. Kay, J., Reimann, P., Diebold, E., & Kummerfeld, B. (2013). MOOCs: So Many Learners, So Much Potential… IEEE Intelligent Systems, 28(3), 70–77. doi: http://dx.doi.org/10.1109/MIS.2013.66

  23. 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.

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

  25. 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.

  26. Lera-López, F., Faulin, J., Juan, A., & Cavaller, V. (2009). Monitoring Students’ Activity and Performance in Online Higher Education: A European Perspective. In A. Juan, A. Daradoumis, F. Xhafa, S. Caballé, & J. Faulin (Eds.), Monitoring and Assessment in Online Collaborative Environments: Emergent Computational Technologies for E-Learning Support (pp. 132–148). IGI Global.

  27. Long, P., Siemens, G., Conole, G., & Gašević, D. (2011). Proceedings of the 1st International Conference on Learning Analytics and Knowledge. Banff, Alberta, Canada. doi: http://dx.doi.org/10.1145/2090116

  28. 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: http://dx.doi.org/10.1002/cae.20468

  29. McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60–66.

  30. 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. doi: http://dx.doi.org/10.1007/978-3-642-40495-5_65

  31. Peña-Ayala, A. (2014). Educational Data Mining: Applications and Trends. New York, NY: Springer. doi: http://dx.doi.org/10.1007/978-3-319-02738-8

  32. Romero, C., & Ventura, S. (2006). Data Mining in E-learning. Southampton, UK: Wit-Press. doi: http://dx.doi.org/10.2495/1-84564-152-3

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

  34. 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: http://dx.doi.org/10.1109/TSMCC.2010.2053532

  35. Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3, 12–27. doi: http://dx.doi.org/10.1002/widm.1075

  36. Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. D. (Eds.) (2010). Handbook of Educational Data Mining. Boca Ratón, FL: CRC Press. doi: http://dx.doi.org/10.1201/b10274

  37. Siemens, G. (2013). Massive Open Online Courses: Innovation in Education? In R. McGreal, W. Kinuthia, & S. Marshall (Eds.), Open Educational Resources: Innovation, Research and Practice (pp. 5–16). Vancouver, Canada: Commonwealth of Learning and Athabasca University.

  38. 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). doi: http://dx.doi.org/10.1145/2330601.2330661

  39. Snijders, C., Matzat, U., & Reips, U.-D. (2012). “Big data”: Big gaps of knowledge in the field of Internet. International Journal of Internet Science, 7, 1–5.

  40. Sonwalkar, N. (2013). The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture — Part I. MOOCs Forum, 1, 22–29.

  41. 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). doi: http://dx.doi.org/10.1145/1013367.1013369

  42. 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 Ratón, FL: CRC Press.

  43. 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.

  44. Wolf, M. A., Jones, R., Hall, S., & Wise, B. (2014). Capacity enablers and barriers for learning analytics: implications for policy and practice. Retrieved from http://all4ed.org/reports-factsheets/capacity-enablers-and-barriers-for-learning-analytics-implications-for-policy-and-practice/

  45. 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.

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Correspondence to Laura Calvet Liñán.

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Calvet Liñán, L., Juan Pérez, Á.A. Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. Int J Educ Technol High Educ 12, 98–112 (2015). https://doi.org/10.7238/rusc.v12i3.2515

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Keywords

  • Online Learning
  • Educational Data Mining
  • Learning Analytics
  • Big Data

Palabras clave

  • aprendizaje en linea
  • minería de datos educativos
  • análisis de datos sobre aprendizaje
  • macrodatos