Learner-content interaction and student performance in undergraduate Economics classes: The case for adaptive learning

Olubunmi Ipinnaiye and Angelica Risquez – University of Limerick

Poor student engagement remains a critical issue in the higher education sector due to its detrimental effect on learning and performance (Bowden et al., 2021). The vital role of various interaction types (e.g. learner-learner, learner-instructor and learner-content) in enhancing student engagement has been acknowledged by several researchers (e.g. Moore, 1989; Riggs, 2020). Arguably, in the context of ever-increasing student-staff ratios, greater attention needs to be placed on the potential impact of learner-content interaction – defined as time spent with course content – in student engagement. Nevertheless, empirical research on learner-content interaction remains sparse (Kumar et al., 2021).

Studies have shown that many higher education students struggle with reading recommended texts (St Clair-Thompson et al., 2018). This issue is even more pronounced in undergraduate Economics students who struggle with abstract and often mathematical content (Nepal & Rogerson, 2020). Whilst access to knowledge is essential, the cognitive processes through which the learner deals with such acquired knowledge is even more crucial (Dunlap et al., 2007). Hence, it is not sufficient to ensure learners’ access to knowledge by merely making learning content available on a course management system. Rather, a deliberate creation of learning tasks which stimulate student engagement in higher-level cognitive behaviour and allow for significant leaner-content interactions is required (Dunlap et al., 2007). This study examines adaptive learning, which involves the tailoring of taught content to match the specific needs of learners, as a valuable pedagogical tool for addressing the need for increased interactions between students and course content, and boosting student performance.

Specifically, the paper explores the use of an adaptive learning tool, McGraw-Hill LearnSmart to design five weekly adaptive reading assignments over a six-week period in a large first year Macroeconomics course. The reading assignments were created to increase student engagement with the course content and encourage deep learning of threshold concepts (Meyer & Land, 2005) in the first half of the course. Student performance was assessed using two timed online tests and the adaptive reading assignments. The ordinary least squares regression method was employed to analyse learning analytics data obtained from McGraw-Hill for a cohort of 606 first year Business students. We find that the amount of time spent studying and the number of adaptive reading assignments completed which capture learner-content interaction and adaptive learning respectively have a positive effect on student performance, measured in turn as student scores on the timed tests and the reading assignments. From a pedagogical perspective, our results highlight the importance of adopting a more deliberate approach in the design of meaningful learning tasks which can potentially help increase learner-content interaction and student performance, particularly in large undergraduate classes.