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

Hlosta, Martin; Herodotou, Christothea; Fernandez, Miriam and Bayer, Vaclav (2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, the Netherlands, June 14-18, 2021, Proceedings, Part II (Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose and Dimitrova, Vania eds.), Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 190–195.

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

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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