Ph.D. Student: Alejandro Bogarín
Advisors: Cristóbal Romero, Rebeca Cerezo
Defended on: September 2018
Keywords: educational data mining, process mining, clustering
Digital version


In this thesis, we propose to use Educational Process Mining (EPM) techniques to discover, analyze and provide a visual representation of the complete educational process in order to make unexpressed knowledge explicit and to facilitate better understanding of the educational process.

The main objective of this thesis is to obtain models which can reproduce the behavior stored in the Moodle log files by students and improve their comprehensibility for instructors. In order to achieve this objective we have defined the next sub-objectives:

  • To preprocess Moodle event logs in order to apply PM techniques.
  • To obtain clusters of students from different information sources in order to improve the comprehensibility of the discovered models.
  • To apply PM techniques and analyze quality forces in order to evaluate how good an educational process model describes the observed data.
  • To create a high level codification with only five labels/actions (Planning, Learning, Forum Peer Learning, Executing, and Review) in order to obtain more understandable models.
  • To compare the most used algorithms in EPM in order to be able to obtain better process models (more specific and accurate) about students’ behaviour when using VLE.
  • To obtain models by chapters in order to analyze of student’s behavior exhaustively.


The development of this thesis is being supported by:

  • Spanish Ministry of Science and Competitiveness, project TIN-2014-55252-P.
  • Department of Science and Innovation under the National Program for Research, Development, and Innovation: EDU2014-57571-P.


  1. A. Bogarin, C. Romero, and R. Cerezo. (2016). Applying data mining to discover common learning routes in moodle. Aplicando minería de datos para descubrir rutas de aprendizaje frecuentes en Moodle. Revista Edmetic. (73-92).
  2. A. Bogarín, R. Cerezo, and C. Romero. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1). (pp. e1230).
  3. A. Bogarín, R. Cerezo, and C. Romero. (2018). Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs). Psicothema, 30(3). (pp. 322-329). DOI: 10.7334/psicothema2018.116
  4. R. Cerezo, A. Bogarín, M. Esteban, and C. Romero. (2019). Process mining for self-regulated learning assessment
    in e-learning. Journal of Computing in Higher Education. DOI: 10.1007/s12528-019-09225-y
  1. C. Romero, R. Cerezo, A. Bogarín, and M. Sánchez-Santillán. (2016). Educational process mining: A tutorial and case study using moodle data sets. Data Mining and Learning Analytics: Applications in Educational Research. (1-28).
  1. A. Bogarín, C. Romero, R. Cerezo, and M. Sánchez-Santillán. (2014). Clustering for improving educational process mining. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 11-15).
  2. R. Cerezo, C. Romero, A. Bogarín, and J.C. Núñez. (2014) Improving performance and comprehensibility of Educational Process Mining models for a better understanding of the learning process. Metacognition 2014. 6th Bienal Meeting of the EARLI Special Interest Group 16. Estambul, Turquia. (pp. 1-2).
  3. A. Bogarín, C. Romero, and R. Cerezo. (2015). Discovering Students’ Navigation Paths in Moodle. In International Conference on Educational Data Mining, Madrid (pp. 556-557).

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