Impacts of engagement on academic outcomes in technology-enhanced learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Engagement is essential for improving academic outcomes, especially in technology-enhanced learning (TEL) environments where self-regulated learning is critical. This study investigated the longitudinal impacts of different levels of engagement on undergraduate students’ short-term and long-term academic outcomes in TEL. Using a learning analytics and learning management system (LMS) log data obtained from a large university blended course, the research (1) identified six key TEL engagement variables, (2) clustered students into four distinct learning profiles based on different levels of TEL engagement, and (3) tracked their summative assessment scores over a term. Material access, formative quiz attempts, and system logins were significant predictors of course performance. The results revealed that TEL differences among student groups, initially minor, expanded over the term, and finally significantly impacted the final exam scores, with highly engaged undergraduates achieving better outcomes. The findings highlight the long-term benefits of fostering TEL or academic success.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it