Enhancing Engagement in a Learning Management System through a Raffle Ticket System
Bibliographic record
Abstract
Learning management systems (LMS) are essential components of courses, yet student engagement on these platforms remains a significant challenge. Traditionally, grades have been used to incentivize student engagement, but this approach comes with drawbacks. In this paper, we explored an alternative form of incentivization, gamification, that has gained traction in higher education. We implemented a gamified raffle ticket system aimed at enhancing asynchronous engagement with our LMS, Microsoft Teams, in a third-year undergraduate elective health sciences course during the fall 2023 semester. We defined engagement as students’ voluntary participation and interaction in LMS-related activities. Students earned raffle tickets for various participatory activities using the LMS—including making a post, replying to a thread, and reacting to posts—which were later entered into a drawing for prizes. Student participation in these activities was tallied and used as quantitative data of engagement. We recorded engagement metrics for three different semesters of the same course: fall 2023 in which the raffle ticket system was implemented; and fall 2021 and fall 2022 before the implementation of the raffle ticket system. Results indicated a substantial increase in engagement metrics following the raffle ticket system’s introduction. Our findings suggest that the gamified raffle ticket system effectively incentivized student engagement, fostering a more interactive and supportive online learning community in the LMS. Thus, this paper offers a framework for educators who are looking to adopt a gamification strategy into their own courses to engage students to interact in an LMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".