Student Success in Asynchronous STEM Education: measuring and identifying contributors to learner outcomes
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
Existing experience with educational institutions that track student outcomes in relation to the time spent on formative activities has demonstrated a consistent positive correlation with improved student outcomes. This study is centred on Higher Education using data from the asynchronous learning platform Möbius, and produces a data-driven validation of the positive correlation between time spent on formative activities and improved student outcomes in an asynchronous online learning environment, and further builds a basis of insights identified as contributors to successful student outcomes. We identified “distance” in time of first engagement in study before an Exam as an early indicator of success, where we found a moderate positive correlation, whereas there was a weak correlation for the same behavior with respect to Practice Tests and Tests. The behaviors identified in this study raised more opportunities for further study on the nature of student engagement through their learning progression, and other early behavioral indicators for successful student outcomes.
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 | 0.001 | 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 | 0.001 | 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