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Learning Analytics Dashboard Analysing First-Year Engineering Students

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Lifelong Technology-Enhanced Learning (EC-TEL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11082))

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Abstract

Nowadays, the higher education institutions experience the problem of the student drop-out. In response to this problem, universities started employing analytical dashboards and educational data mining methods such as machine learning, to detect students at risk of failing their studies. In this paper, we present interactive web-based Learning Analytics dashboard - Analyst, which has been successfully deployed at Faculty of Mechanical Engineering (FME), Czech Technical University in Prague. The dashboard provides academic teaching staff with the opportunity to analyse student-related data from various sources in multiple ways to identify those, who might have difficulties to complete their degree. For this purpose, multiple analytical dashboard views have been implemented. It includes summary statistic, study progression graph, and credit completion probabilities graph. In addition, users have the option to export all analysis related graphs for the future use. Based on the outcomes provided by the Analyst, the university successfully ran the interventions on the selected at-risk students and significantly increased the retention rate in the first study year.

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Notes

  1. 1.

    http://shiny.rstudio.com.

  2. 2.

    https://www.R-project.org/.

  3. 3.

    https://CRAN.R-project.org/package=plotly.

  4. 4.

    http://ggplot2.org/.

  5. 5.

    https://analyse.kmi.open.ac.uk/resources/documents/letter_of_recognition.pdf.

References

  1. Quinn, J.: Drop-out and completion in Higher Education in Europe (2013). https://edudoc.ch/record/110174/files/dropout.pdf

  2. Shacklock, X.: From bricks to clicks: the potential of data and analytics in higher education (2016)

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  4. Jivet, I., Scheffe, M., Specht, M., Drachsler, H.: License to evaluate: Preparing learning analytics dashboards for educational practice. In: Proceedings of the Eight International Conference on Learning Analytics and Knowledge, Sydney (2018)

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  5. Zdrahal, Z., Hlosta, M., Kuzilek, J.: Analysing performace of first year engineering students. In: Proceedings of the Data Literacy for Learning Analytics Workshop, Edinburgh (2016)

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Acknowledgement

This work was supported by junior research project by Czech Science Foundation GACR no. GJ18-04150Y.

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Correspondence to Jonas Vaclavek .

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Vaclavek, J., Kuzilek, J., Skocilas, J., Zdrahal, Z., Fuglik, V. (2018). Learning Analytics Dashboard Analysing First-Year Engineering Students. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_48

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  • DOI: https://doi.org/10.1007/978-3-319-98572-5_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98571-8

  • Online ISBN: 978-3-319-98572-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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