Integrating National Benchmark Test (NBT) diagnostics into data analytics reports

Student retention and academic success remain significant challenges in South African higher education (Council on Higher Education (CHE), 2023: 12), and the articulation gap between school and university has been identified as a central concern. Addressing these challenges requires data-driven evaluation techniques. These data-driven techniques, such as diagnostic assessments, use empirical information to make important decisions affecting students’ educational progress, help teachers improve their educational curricula, and identify areas requiring improvement by monitoring and integrating information relating to a student’s educational progress (Aburizaizah, 2021). The Diagnostic Mathematics Information for Student Retention and Success (DMISRS) project was initially conceptualised to explore the articulation gap between students’ Grade 12 exit level skills in the areas of mathematics, academic literacy and quantitative literacy and the entry-level demands of higher education study, and to investigate ways of addressing this gap. A key subproject of the DMISRS project examined the influence of National Benchmark Test (NBT) subdomains on student success and how this diagnostic information could be shared and used by educators across the sector. The NBT consists of three domains: Academic Literacy (AL), Quantitative Literacy (QL), and Mathematics (MAT), each with specific subdomains. Subdomain analyses and relative importance analyses (RIA) provide granular insights into student performance, highlighting the relevance of subdomains for course success and the skills students need to succeed. By identifying areas needing attention, these analyses provide educators with information that they can use to tailor student support and effectively adapt their classroom practices. These analyses are situated within an Integrated Framework of Diagnosis, which integrates assessment inputs, analytical methods, interpretation and reporting mechanisms, and application in teaching and learning. The framework emphasises the feedback loop among four components: diagnostics, feedback, remediation / interventions, and validation. This paper presents the integration of diagnostic information using case studies, which include RIA and subdomain performance analyses of a South African university’s data analytics system referred to as the Data Analytics for Student Success (DASS). It highlights how, with the appropriate support to link this diagnostic information with curricula, access to it can help inform teaching and learning support initiatives. Drawing on the DASS example and the DMISRS/NBT diagnostic project, the paper further demonstrates how such diagnostic outcomes can be mobilised to provide more targeted, curriculum-integrated forms of student support.