Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs)

• Alejandro Bogarín ^[2] ; Rebeca Cerezo ^[1] ; Cristóbal Romero ^[2]
  1. [1] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  2. [2] Universidad de Córdoba
• Localización: Psicothema, ISSN-e 1886-144X, ISSN 0214-9915, Vol. 30, Nº. 3, 2018, págs. 322-329
• Idioma: inglés
• Títulos paralelos:
  • Descubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs)
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 5 Citas
• Resumen
  • español

    Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner.

    Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos

  • English

    Background: Process mining with educational data has made use of various algorithms for model discovery, principally Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner. In this study we propose the implementation of a new algorithm for educational data called Inductive Miner. Method:

    We used data from the interactions of 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform’s event logs; following preprocessing, the mining was carried out on 21,629 events to discover what models the various algorithms produced and to compare their fi tness, precision, simplicity and generalization. Results: The Inductive Miner algorithm produced the best results in the tests on this dataset, especially for fi tness, which is the most important criterion in terms of model discovery. In addition, when we weighted the various metrics according to their importance, Inductive Miner continued to produce the best results. Conclusions: Inductive Miner is a new algorithm which, in addition to producing better results than other algorithms using our dataset, also provides valid models which can be interpreted in educational terms

• Referencias bibliográficas
  • Areces, D., Rodríguez Muñiz, L. J., Suárez Álvarez, J., de la Roca, Y., & Cueli, M. (2016). Information sources used by high school students...
  • Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ selfregulated...
  • Bogarín, A., Romero, C., Cerezo, R., & Sánchez-Santillán, M. (2014). Clustering for improving educational process mining. In M. Pistilli,...
  • Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and...
  • Bose, R. J. C., & van der Aalst, W. M. (2009, September). Trace clustering based on conserved patterns: Towards achieving better process...
  • Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning...
  • Buijs, J. C., Van Dongen, B. F., & van Der Aalst, W. M. (2012). On the role of fitness, precision, generalization and simplicity in process...
  • Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovski, A. Kobsa...
  • Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students’ LMS interaction patterns and their relationship...
  • Cerezo, R., Núñez, J. C., Rosario, P., Valle, A., Rodríguez, S., & Bernardo, A. (2010). New Media for the promotion of self-regulated...
  • Dahlstrom, E., Brooks, D. C., & Bichsel, J. (2014). The current ecosystem of learning management systems in higher education: Student,...
  • Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991-16005. doi:10.1109/...
  • Emond, B., & Buffett, S. (2015, June). Analyzing Student Inquiry Data Using Process Discovery and Sequence Classifi cation. Paper presented...
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications...
  • Leemans, S. J., Fahland, D., & van der Aalst, W. M. (2013, August). Discovering block-structured process models from event logs containing...
  • Leemans, S. J., Fahland, D., & van der Aalst, W. M. (2014). Process and Deviation Exploration with Inductive Visual Miner. BPM (Demos),...
  • Lust, G., Elen, J., & Clarebout, G. (2013a). Regulation of tool-use within a blended course: student differences and performance effects....
  • Lust, G., Elen, J., & Clarebout, G. (2013b, August). Measuring students’ strategy-use within a CMS supported course through students’...
  • Mukala, P., Buijs, J. C. A. M., & Van Der Aalst, W. M. P. (2015). Uncovering learning patterns in a MOOC through conformance alignments...
  • Mukala, P., Buijs, J. C., Leemans, M., & van der Aalst, W. M. (2015, December). Learning Analytics on Coursera Event Data: A Process Mining...
  • Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review...
  • Pechenizkiy, M., Trcka, N., Vasilyeva, E., van Aalst, W., & De Bra, P. (2009, July). Process mining online assessment data. In Educational...
  • Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications,...
  • Reimann, P., Markauskaite, L., & Bannert, M. (2014). E-Research and learning theory: What do sequence and process mining methods contribute?...
  • 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. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics,...
  • Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1),...
  • Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers &...
  • Romero, C., Cerezo, R., Bogarín, A., & Sánchez-Santillán, M. (2016). Educational process mining: a tutorial and case study using Moodle...
  • Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion...
  • Sanmamed, M. G., Carril, P. C. M., & Álvarez De Sotomayor, I. D. (2017). Factors which motivate the use of social networks by students....
  • Trcka, N., & Pechenizkiy, M. (2009). From local patterns to global models: Towards domain driven educational process mining. In Proceedings...
  • Trcka, N., Pechenizkiy, M., & van der Aalst, W. (2010). Process mining from educational data. In C. Romero, S. Ventura, M. Pechenizkiy...
  • van der Aalst, W. M. (2011). Process Discovery: An Introduction. In Process Mining (pp. 125-156). Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-19345-3_5
  • van der Aalst, W. M. (2016). Process mining: data science in action. Berlin, Heidelberg: Springer. doi:10.1007/978-3-662-49851-4
  • van der Aalst, W. M., Schonenberg, M. H., & Song, M. (2011). Time prediction based on process mining. Information systems, 36(2), 450475.
  • van Dongen, B. F. (2007). Process mining and verification. Dissertation Abstracts International, 68(4).
  • Vidal, J. C., Vázquez-Barreiros, B., Lama, M., & Mucientes, M. (2016). Recompiling learning processes from event logs. Knowledge-Based...
  • Weijters, A.J.M.M., van Der Aalst, W.M., & De Medeiros, A.A. (2006). Process mining with the heuristics miner-algorithm. Technische Universiteit...
  • Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational psychologist, 25(1), 3-17.