Using Learner Analytics to Explore the Potential Contribution of Multimodal Formative Assessment to Academic Success in Higher Education
Bibliographic record
Abstract
Blended learning pedagogical practices supported by learning management systems have become an important part of higher education curricula. In most cases, these blended curricula are evaluated through multimodal formative assessments. Although assessments can strongly affect student outcomes, research on the topic is limited. In this paper, we adopted a learning analytics approach to explore student engagement with formative assessments and the power of these assessments to predict student outcomes in an English for Academic Purposes courses in a Hong Kong university. The study retrieved the data logs from 7,815 students and used the data to analyze student engagement with the formative assessments. The results suggested that the students put effort into completing the assessments. The degree to which assessments predict learning outcomes depend on students’ level of subject knowledge and their understanding of the relevance of the assessments. This study showed that learning analytics provided reliable evidence for understanding students’ engagement and identifying at-risk students. Therefore, learning analytics research has the potential to inform pedagogical practice.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".