The impact of frequency and stakes of formative assessment on student achievement in higher education: A learning analytics study
Bibliographic record
Abstract
Abstract Background Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress. Optional assessments may encourage voluntary engagement, potentially leading to a more genuine reflection of student understanding. Also, frequent assessments provide continuous opportunities for feedback and adjustment, which can keep students actively engaged in the learning process. Objectives This study aims to investigate two crucial facets of formative assessments: frequency and the level of stakes involved (mandatory vs. optional). We examine how modifying the frequency of formative assessments affects students' course performance. Additionally, we evaluate the impact of mandatory versus optional formative assessments on students' course performance in higher education. Methods The sample of this study consisted of undergraduate students (n = 336) enrolled in three sections of a large asynchronous course at a Canadian university. We extracted features associated with online formative assessments (e.g., the number of attempts and average scores) from the learning management system. Next, we used these features to predict students' performance in summative assessments (two midterms and a final exam). Results and Conclusions Our findings indicated that increasing the frequency of online formative assessments did not consistently improve student performance. Also, participation frequency in online formative assessments seemed to vary depending on assessment stakes (i.e., optional vs. mandatory). We recommend that instructors examine what conditions can maximize the contribution of formative assessments to students' academic achievement before building predictive models.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".