WEEKLY LOW-STAKES ASSESSMENTS PROMOTE STUDENT MOTIVATION, ENGAGEMENT, AND LEARNING IN ASYNCHRONOUS ONLINE COURSES
Bibliographic record
Abstract
Since the COVID-19 pandemic, many universities are increasing offerings of asynchronous online courses, including encouraging the modification of existing courses into an online format. With a move from in-person to online course delivery comes the challenge of maintaining and creating new ways of promoting student motivation and engagement to facilitate their attainment of course learning goals. This paper discusses the development and impact of an instructional intervention made in two large-enrollment active learning introductory courses at a Canadian university that were transformed to an asynchronous online course modality during and after the COVID-19 pandemic. In-class activities were redesigned based on best practices in online teaching into weekly low-stakes, formative "class engagement activities" (CEAs). The study used a mixed-methods research design to understand the impact that CEAs have on student motivation, engagement, and perceptions of learning. Results demonstrate that despite the low grading weight of the CEAs, the activities achieved high levels of student engagement, which impacted final exam performance, motivation to learn, and a perceived deeper understanding of course content. We conclude that CEAs are a relatively low-effort strategy for instructors to engage students in their course materials in the asynchronous online course environment and recommend best practices for incorporating these assignments into course design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".