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Adaptation of a Process Mining Methodology to Analyse Learning Strategies in a Synchronous Massive Open Online Course

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Information and Communication Technologies (TICEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1648))

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Abstract

The study of learners’ behaviour in Massive Open Online Courses (MOOCs) is a topic of great interest for the Learning Analytics (LA) research community. In the past years, there has been a special focus on the analysis of students’ learning strategies, as these have been associated with successful academic achievement. Different methods and techniques, such as temporal analysis and process mining (PM), have been applied for analysing learners’ trace data and categorising them according to their actual behaviour in a particular learning context. However, prior research in Learning Sciences and Psychology has observed that results from studies conducted in one context do not necessarily transfer or generalise to others. In this sense, there is an increasing interest in the LA community in replicating and adapting studies across contexts. This paper serves to continue this trend of reproducibility and builds upon a previous study which proposed and evaluated a PM methodology for classifying learners according to seven different behavioural patterns in three asynchronous MOOCs of Coursera. In the present study, the same methodology was applied to a synchronous MOOC on edX with N = 50,776 learners. As a result, twelve different behavioural patterns were detected. Then, we discuss what decision other researchers should made to adapt this methodology and how these decisions can have an effect on the analysis of trace data. Finally, the results obtained from applying the methodology contribute to gain insights on the study of learning strategies, providing evidence about the importance of the learning context in MOOCs.

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Acknowledgments

This paper was supported by the ANR LASER (156322) and Vicerrectorado de Investigación de la Universidad de Cuenca. The authors acknowledge PROF-XXI, which is an Erasmus+ Capacity Building in the Field of Higher Education project funded by the European Commission (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP).

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Authors and Affiliations

  1. Department of Computer Science, Universidad de Cuenca, Cuenca, Ecuador

    Jorge Maldonado-Mahauad

  2. Department of Telematics Engineering, Universidad Carlos III de Madrid, Getafe, Spain

    Carlos Alario-Hoyos & Carlos Delgado Kloos

  3. Institute de Recherce Informatique de Toulouse, Université de Toulouse, Toulouse, France

    Mar Perez-Sanagustin

Authors
  1. Jorge Maldonado-Mahauad
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  2. Carlos Alario-Hoyos
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  3. Carlos Delgado Kloos
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  4. Mar Perez-Sanagustin
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Corresponding author

Correspondence to Jorge Maldonado-Mahauad .

Editor information

Editors and Affiliations

  1. Universidad Laica Eloy Alfaro de Manabí, Manta, Ecuador

    Jorge Herrera-Tapia

  2. Universidad Técnica Particular de Loja, Loja, Ecuador

    Germania Rodriguez-Morales

  3. Universidad de las Fuerzas Armadas ESPE, Quito, Ecuador

    Efraín R. Fonseca C.

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta-Guzman

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Maldonado-Mahauad, J., Alario-Hoyos, C., Delgado Kloos, C., Perez-Sanagustin, M. (2022). Adaptation of a Process Mining Methodology to Analyse Learning Strategies in a Synchronous Massive Open Online Course. In: Herrera-Tapia, J., Rodriguez-Morales, G., Fonseca C., E.R., Berrezueta-Guzman, S. (eds) Information and Communication Technologies. TICEC 2022. Communications in Computer and Information Science, vol 1648. Springer, Cham. https://doi.org/10.1007/978-3-031-18272-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-18272-3_9

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