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The purpose of this paper is to describe examples of the application of learning analytics (LA), including the assessment of subject grades, identifying subjects that need revision, student satisfaction and cohort comparisons, to program curriculum review.

Examples of analyses that address specific questions that a curriculum review wishes to address are provided, together with examples of visualizations from the analyses to aid interpretation.

The results show that using LA as a part of curriculum review can provide insights not possible with the traditional curriculum review methods and can yield useful and actionable insights.

The work in this paper illustrates another important application for LA and demonstrates the value this approach has for informing curriculum enhancement at the program level.

The analyses described provide insights not possible with traditional curriculum review methods. However, the challenge remains to develop analytic tools that can assist teachers to conduct LA independently.

LA have been used to predict grades or identify at-risk students (Gaševic et al., 2016), but there is little research on its use for curriculum evaluation (Méndez et al., 2014). This paper addresses this gap and provides examples of its application in program curriculum review and the insights it can provide.

The purpose of this paper is to report undergraduate students’ learning gains in six areas of generic skills. The paper reports on students’ responses to the First Year Experience (FYE) Survey completed at the end of their first year and Graduating Student Survey (GSS) in the final semester of their final year.

In this study, a longitudinal design was applied in data collection, analysis and reporting of assessment if student learning gains. The undergraduate students who were the first cohort of four-year curriculum students in a Hong Kong university were selected as the sample. Repeated measures of reported learning gains of a longitudinal sample based on stacking of both FYE and GSS data were analysed using the Rasch model.

The results showed that the scale for measuring the six areas of generic skills had high reliability and good person separation. Comparison of repeated measures from the same group of students at the two time points were examined to explore whether there is growth in the generic skills during their university studies.

One limitation of the study was the relatively small sample size of 359 students in one higher education institution.

The findings of the study provide insight into conceptual understanding and measurement of university student learning gains.

Whilst several studies have investigated university student learning gains, there is limited research which explores the use Rasch modelling in assessment of student learning gains in multiple areas towards completion of their undergraduate studies.