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  5. Understanding the BAME attainment gap at the OU by means of quantitative and qualitative data analytics

Understanding the BAME attainment gap at the OU by means of quantitative and qualitative data analytics

Project leader(s): 
Miriam Fernandez, Martin Hlosta and Tracie Farrell

Data-driven, student-centered approaches can reduce attainment gaps in higher education [1]. This project aims to investigate the attainment gap for Black, Asian and Minority Ethnic (BAME) students at the Open University (OU)1 by means of a combination of qualitative and quantitative data analytics.

At the moment, when learning analytics are applied to learn student patterns, and identify students at risk, data is not disaggregated by groups. Divergences in the learning and behavioural patterns of minority groups (BAME students) may hence not be given the same relevance than the ones of the majority group. This problem has been highlighted by scholars in other fields. E.g., Criado-Perez [2] shows how women are 50% more likely to die of a heart-attack. Symptoms for women are different than the ones for men, but more men suffer heart attacks. Because the symptoms of the majority group (in this case men) have been considered to design medical guidelines, women are more likely to be misdiagnosed and die during a heart attack. A similar issue may be happening in HE, and more particularly at the OU. Since White students form the majority group at the OU [3] we may not be considering the needs of students from minority backgrounds when designing our educational practices and interventions, hence contributing to existing inequalities. Previous work at The OU [4] on traces of 150,000 students confirms the existence of an attainment gap of BME students, who are 19%-79% less likely to achieve excellent grades. More important BME students spent 4-12% more of study time to achieve the same academic performance as white students. This project aims to understand pattern divergences between BAME and non-BAME students by conducting large-scale data analytics and use the findings of these analysis to drive focus groups with students and members of staff.

1This information is drawn from self-designations of ethnicity on enrollment forms. The acronym BAME may not be a preference of all students designated as BAME by the institution (


[1] Panesar, L., 2017. Academic support and the BAME attainment gap: using data to challenge assumptions. Spark: UAL Creative Teaching and Learning Journal, 2(1), pp.45-49

[2] Criado-Perez, C. (2017). Invisible Women: Exposing Data Bias in a World Designed for Men. Random House.


[4] Nguyen Q., Rienties B. Richardson J.T.E. (2020) Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting, Assessment & Evaluation in Higher Education, 45:4, 594-606, DOI: 10.1080/02602938.2019.1679088

Miriam Fernandez, Martin Hlosta and Tracie Farrell poster (PPTX)