Data and analytics are often thrown together as solutions to a range of higher education problems, but often with little specific detail. But learning data can offer the potential to address an ongoing challenge in higher education: what are students gaining from their time and investment in higher education?

A decade ago, efforts to measure learning gain in England commenced, focusing on assessing the changes in students' knowledge, skills, work-readiness, and personal development, as well as improvements in specific practices and outcomes within particular disciplinary and institutional contexts. These initiatives were driven by the government which sought to determine the value it was deriving from the investment in higher education (Department for Business, Innovation and Skills, 2016). Pilot projects were overseen by the Higher Education Funding Council for England (HEFCE) and later transitioned to the Office for Students (OfS).

Through a series of pilot projects, three key dimensions of learning gain were identified:
(1) measures of general cognitive development, encompassing students' knowledge and critical thinking;
(2) measures of soft skills development, including affective indicators of attitudes, how students feel, and behavioral measures of their engagement; and
(3) measures of employability and career readiness, primarily focusing on behavioral indicators of students' activities in preparation for the workforce.

Despite these efforts, significant challenges arose in the measurement of learning gain, including low student participation in supplementary assessments and surveys, variations in students' starting points, and the lack of a standardized baseline across different courses and institutions.

Learning analytics present a potential solution to addressing the challenges in measuring learning gain. Significant progress has been made in the field of learning analytics, which involves the measurement, collection, analysis, and reporting of data related to learners' progress and the contexts in which learning occurs. With the emergence of generative AI models, learning analytics can expand further, incorporating a broader range of data sources to enhance the understanding of student learning.

The ethical use of learning analytics is essential. This paper presents findings from a partnership project with staff and students to develop guidelines and policies for the ethical use of learning analytics. We received institutional funding for a year-long project supporting students as co-researchers. Staff and students worked in collaboration to conduct focus groups with students about data use, analytics and interventions.

Data about learning does not inherently provide benefit to students; it depends on how the data is used for enhancement. The findings from the study with students provided insight into how, in partnership with students, we could use student learning data to support students to understand what they have gained from their higher education experience. We identified the importance of making the data understandable and presented in ways a broad audience could understand. If the analytics and outputs are too complex and confusing for staff and students to understand and apply them, they will have limited impact. Students and academic staff need support, advice and guidance to use educational gain data.

Keywords: Learning analytics, student outcomes, learning gains.