Learning Analytics with Small Datasets-State of the Art and Beyond - Ngoc Buu Cat Nguyen; Thashmee Karunaratne (MDPI AG, 2024)
Although learning analytics (LA) often processes massive data, not all courses in higher education institutions are on a large scale, such as courses for employed adult learners (EALs) or master's students. This places LA in a new situation with small datasets. This paper explores the contemporary situation of how LA has been used for small datasets, whereby we examine how the observed LA provisions can be validated in practice, which opens up possible LA solutions for small datasets and takes a further step from previous studies to enhance this topic. By examining the field of LA, a systematic literature review on state-of-the-art LA and small datasets was conducted. Thirty relevant articles were selected for the final review. The results of the review were validated through a small-scale course for EALs at a Swedish university. The findings revealed that the methods of multiple analytical perspectives and data sources with the support of contexts and learning theories are useful for strengthening the reliability of results from small datasets. More empirical evidence is required to validate possible LA methods for small datasets. The LA cycle should be closed to be able to further assess the goodness of the models generated from small datasets.