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Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged Students in STEM

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Artificial Intelligence in Education (AIED 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12749))

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Abstract

In this work, we investigate the degree-awarding gap in distance higher education by studying the impact of a Predictive Learning Analytics system, when applying it to 3 STEM (Science, Technology, Engineering and Mathematics) courses with over 1,500 students. We focus on Black, Asian and Minority Ethnicity (BAME) students and students from areas with high deprivation, a proxy for low socio-economic status. Nineteen teachers used the system to obtain predictions of which students were at risk of failing and got in touch with them to support them (intervention group). The learning outcomes of these students were compared with students whose teachers did not use the system (comparison group). Our results show that students in the intervention group had 7% higher chances of passing the course, when controlling for other potential factors of success, with the actual pass rates being 64% vs 61%. When disaggregated: 1) BAME students had 10% higher pass rates (55 %vs 45%) than BAME students in the comparison group and 2) students from the most deprived areas had 4% higher pass rates (58% vs 54%) in the intervention group compared to the comparison group.

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Notes

  1. 1.

    In the UK, the SES gap can be expressed as a difference between students from low and high deprived areas, measured by Index of Multiple Deprivation (IMD) [9, 12].

  2. 2.

    The results only include attributes where at least one of the factors had \(p<0.05\). The full analysis can be found at https://doi.org/10.21954/ou.rd.14414774.

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Authors and Affiliations

  1. The Open University, Milton Keynes, UK

    Martin Hlosta, Christothea Herodotou, Vaclav Bayer & Miriam Fernandez

Authors
  1. Martin Hlosta
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  2. Christothea Herodotou
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  3. Vaclav Bayer
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  4. Miriam Fernandez
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Corresponding author

Correspondence to Martin Hlosta .

Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Haifa, Israel

    Ido Roll

  2. Arizona State University, Tempe, AZ, USA

    Danielle McNamara

  3. Utrecht University, Utrecht, The Netherlands

    Sergey Sosnovsky

  4. London Knowledge Lab, London, UK

    Rose Luckin

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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Hlosta, M., Herodotou, C., Bayer, V., Fernandez, M. (2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged Students in STEM. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_34

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  • DOI: https://doi.org/10.1007/978-3-030-78270-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78269-6

  • Online ISBN: 978-3-030-78270-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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