Introductory statistics is a gateway course for many degree programs. This study examines which student characteristics and behaviors are associated with better (or worse) performance, to inform course design and student support.
We analyze admin/course records from a first-year statistics class, linking outcomes to attendance, use of online learning materials, prior preparation, and socio-demographics using multivariate models. Results are associative (not all effects are causal), but they provide clear priorities for instruction and support.
Positive associations (higher grades):
Engagement: Regular class attendance and active use of online materials (LMS downloads, practice resources) are the strongest, most consistent positives.
Learner groups who excelled: In this sample, female students and students from outside the capital tended to outperform peers when other factors were held constant.
Negative associations (lower grades):
Time constraints: Employment during term and family obligations systematically reduce available study time and are linked with lower performance.
Low perceived importance: Students who do not view the course as important engage less and earn lower marks.
Preparation gaps: Differences tied to prior schooling tracks (including language of instruction) indicate uneven preparation in foundational math/statistics, which depresses outcomes unless addressed.
Measurement notes: A proxy for consistency of effort (steady week-to-week engagement) was only partially successful—useful in practice, but imperfect as a standalone metric.
The strongest, most reliable levers are attendance, structured weekly engagement, and accessible online practice. Conversely, time scarcity and preparation gaps can erode performance even for motivated students. These insights point to practical, scalable adjustments universities can implement without overhauling curricula.
Reward regular contact: Embed brief in-class retrieval checks or participation credit to make attendance pay off.
Treat the LMS as a second classroom: Provide clear weekly checklists, low-stakes practice, rapid feedback, and visibility into progress.
Bridge preparation gaps early: Use week-1 diagnostics; offer just-in-time refreshers on algebra/probability and targeted tutorial hours.
Design for scarce time: Modularize resources (10–15-minute practice blocks), maintain predictable deadlines, and avoid bunching assessments.
Support constrained students: Offer flexible office hours (including online), study groups, and referral pathways for students juggling work/family.
Akimov, A., Malin, M., Sargsyan, Y., Suyunov, G., & Turdaliev, S. (2023). Student Success in University First Year Statistics Course: Do Student Characteristics Affect Their Academic Performance? Journal of Statistics and Data Science Education, 32(01). https://doi.org/10.1080/26939169.2023.2184435 (ISSN: 2693-9169).