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Using Meta-Learning to predict student performance in virtual learning environments

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Abstract

Educational Data Science has meant an important advancement in the understanding and improvemen of learning models in recent years. One of the most relevant research topics is student performance prediction through click-stream activity in virtual learning environments, which provide abundant information about their behaviour during the course. This work explores the potential of Deep Learning and Meta-Learning in this field, which has thus far been explored very little, so that it can serve as a basis for future studies. We implemented a predictive model which is able to automatically optimise the architecture and hyperparameters of a deep neural network, taking as a use case an educational dataset that contains information from more than 500 students from an online university master’s degree. The results show that the performance of the autonomous model was similar to the traditionally designed one, which offers significant benefits in terms of efficiency and scalability. This also opens up interesting areas of research related to Meta-Learning applied to educational Big Data.

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Code Availability

All code for data analysis and model training associated with the current submission is available at https://colab.research.google.com/drive/1pQNm36nEgA83FGqno-HvJH8AJRpoFmlj?usp=sharing

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Funding

This work has been partially funded by the PLeNTaS project, “Proyectos I+D+i 2019”, PID2019-111430RB-I00 and by Universidad Internacional de La Rioja (UNIR, http://www.unir.net) through the IBM-UNIR Chair on Data Science in Education.

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Authors and Affiliations

  1. Universidad Internacional de La Rioja, Avda. de la Paz, 137, Logroño, La Rioja, Spain

    Ángel Casado Hidalgo, Pablo Moreno Ger & Luis De La Fuente Valentín

Authors
  1. Ángel Casado Hidalgo
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  2. Pablo Moreno Ger
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  3. Luis De La Fuente Valentín
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Contributions

Ángel Casado Hidalgo carried out the experiment and wrote the manuscript with support from Pablo Moreno Ger. Luis De La Fuente Valentín helped to supervise the project. All authors read and approved the final manuscript.

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Correspondence to Ángel Casado Hidalgo.

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The authors declare that they have no conflicts of interest.

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Availability of data and material

The data which supports the findings of this study are available from the International University of La Rioja but restrictions apply to the availability of this data, which was used under license for the current study, and so are not publicly available. However, data is available from the authors upon reasonable request and with the permission of the International University of La Rioja.

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Hidalgo, Á.C., Ger, P.M. & Valentín, L.D.L.F. Using Meta-Learning to predict student performance in virtual learning environments. Appl Intell 52, 3352–3365 (2022). https://doi.org/10.1007/s10489-021-02613-x

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