This systematic review investigated how Higher Education Institutions (HEIs) optimise data
analytics in postgraduate programmes to enhance student achievement. Existing research …

The tremendous growth in electronic educational data creates the need to have meaningful
information extracted from it. Educational Data Mining (EDM) is an exciting research area …

Demographic features are commonly used in Educational Data Mining (EDM) research to
predict at-risk students. Yet, the practice of using demographic features has to be considered …

Rubric-based admissions are claimed to help make the graduate admissions process more
equitable, possibly helping to address the historical and ongoing inequities in the US …

Abstract Letters of Recommendation (LORs) are widely utilized for admission to both
undergraduate and graduate programs, and are becoming even more important with the …

Predicting student performance is crucial for improving students' future academic
achievements. Within student groups, common characteristics can reveal trends in overall …

Studying for entrance examinations can be a distressing period for numerous students.
Consequently, many students decide to attend cram schools to assist them in preparing for …

Abstract Utilisation of Educational Data Mining (EDM) can be useful in predicting academic
performance of students to mitigate student attrition rate, allocation of resources, and aid in …

Although using demographic features for predictive models in Educational Data Mining
(EDM) has to be considered very problematic from a fairness point of view and is currently …

We encounter variables with little variation often in educational data mining (EDM) due to
the demographics of higher education and the questions we ask. Yet, little work has …