Exploring Student Engagement Factors in a Blended Undergraduate Course
Bibliographic record
Abstract
Student engagement is an important factor in academic performance and comprises four dimensions: behavioural, cognitive, emotional (Fredericks et al., 2004), and agentic (Reeve, 2013). Blended courses provide unique opportunities for instructors to use trace data collected during learning to understand and support student engagement. This mixed-methods case study compared the student engagement of two groups of students with a history of low prior academic achievement. The groups were (a) students who ultimately did well in the course and (b) students who did poorly. Data came from two primary sources: (a) log file data from the course LMS, and (b) trace data derived from authentic learning tasks. Data represented five indicators: (a) behavioural engagement, (b) cognitive engagement, (c) emotions experienced during learning, (d) agency or proactive approaches to studying, and (e) overall academic engagement. Findings indicated students who moved achievement groups showed higher levels of behavioural engagement, cognitive engagement, and agentic or proactive approaches to studying and overall engagement. Additionally, students who remained in the low achievement group showed higher levels of positive deactivating emotions (e.g., relief). Implications for future research on student engagement and designing teaching to increase engagement in blended courses are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".