Bibliographic record
Abstract
Undergraduate students living on-campus and taking online and face-to-face courses concurrently, are the predominant consumer of online classes (Seaman et al., 2018). However, they have lower rates of persistence for online courses as compared to face-to-face courses (Hart, 2012; Xu & Jaggars, 2011). Part of the reason could be due to the mismatch between the types of interactions they prefer and what is being provided in online courses. The purpose of this literature review is to investigate the use of asynchronous and synchronous discussions as a way to address the needs of emerging online learners. Using elements of previously developed frameworks, I propose the Framework for Emerging Online Learner Persistence (FEOLP). This framework addresses the values and needs of emerging online learners through course design that has the potential to enhance social presence using student values to determine the blend of asynchronous and synchronous interactions. Given the limited research to draw from on how to design online courses, this framework and the recommendations from this article provide a starting point for the responsive design of online courses for the emerging online learner with potential application to other groups of distinct online learners.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".