One of the more noticeable changes to tertiary teaching over the past decade has been the widespread adoption of digital technologies, in particular eLearning platforms and lecture capture technology. However, much of the current knowledge of how students utilise these new technologies and their effect on traditional lecture attendance is simply derived from student surveys rather than comprehensive independent analyses. In this study, we use cluster analysis to identify common lecture resource utilisation patterns for students in four large first-year business subjects. While common usage patterns with respect to lecture attendance, video lecture recording access and download of lecture notes are identified across our subjects, the proportion of students within each of the utilisation clusters varies widely. Business statistics students are much more likely to either attend lectures or view video recordings compared to economics students, many of whom rely solely on the download of lecture notes. In order to gain insight into how student characteristics may affect these utilisation patterns, we develop a predictive model, quantifying the influences of prior academic performance, gender, age, distance from campus and international student status using statistical modelling. We find a strong role for students’ previous academic performance in explaining lecture resource utilisation patterns. Students’ commuting distance to campus is also established as a factor dissuading physical lecture attendance. Contrary to initial expectations, we also found that females and older students tend to rely more heavily on digital resources rather than lecture attendance. It is hoped that these findings can help first-year instructors and University administrators understand the heterogeneity of student lecture engagement patterns within the first-year experience.
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Namely the motivation, ability, attendance and achievement models (Jones 1984).
Students with missing values for any of the variables were removed from the analysis. Likewise, students who did not attend the final exam were excluded.
We have no insightful explanation for this observation except to note that microeconomics is an elective first-year subject while the other three subjects are compulsory core subjects.
Except in the case of macroeconomics, where we previously observed a much lower average mark.
With the exception of cluster 3 in the statistics subject, denoted with an asterisk in Table 2.
‘High’ frequency is defined as more than 50% of lectures (> 6).
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O’Brien, M., Verma, R. How do first year students utilize different lecture resources?. High Educ 77, 155–172 (2019). https://doi.org/10.1007/s10734-018-0250-5
- Cluster analysis
- Lecture recordings
- Lecture attendance
- Learning analytics
- Student engagement