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A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

Author

Listed:
  • Daniel Amo

    (Engineering Department, Group of Research GRETEL. La Salle, Ramon Llull University, 08022 Barcelona, Spain)

  • Sandra Cea

    (Engineering Department, Group of Research GRETEL. La Salle, Ramon Llull University, 08022 Barcelona, Spain)

  • Nicole Marie Jimenez

    (Engineering Department, Group of Research GRETEL. La Salle, Ramon Llull University, 08022 Barcelona, Spain)

  • Pablo Gómez

    (Engineering Department, Group of Research GRETEL. La Salle, Ramon Llull University, 08022 Barcelona, Spain)

  • David Fonseca

    (Architecture Department, Group of Research GRETEL. La Salle, Ramon Llull University, 08022 Barcelona, Spain)

Abstract

Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption.

Suggested Citation

  • Daniel Amo & Sandra Cea & Nicole Marie Jimenez & Pablo Gómez & David Fonseca, 2021. "A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5085-:d:547554
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    References listed on IDEAS

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    1. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    2. Daniel Amo & Marc Alier & Francisco José García-Peñalvo & David Fonseca & María José Casañ, 2020. "Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    3. Faraway, Julian J. & Augustin, Nicole H., 2018. "When small data beats big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 142-145.
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