Bibliographic record
Abstract
IntroductionThe number of students enrolled in online and distance education courses has been increasing since 2000 (Allen & Seaman, 2013). In the fall of 2015, there were 5,954,121 students enrolled in any distance education courses at degree-granting postsecondary institutions (U.S. Department of Education, 2015). This increase in virtual education had brought about pedagogical changes and adaptations that have altered the roles of the educator and learner, and had reshaped the environment in which they interact. According to the 2017 distance enrollment report by theDigital Learning Compass , the number of students who have enrolled in online courses had surpassed six million nationally, continuing a growth trend that has been consistent for 13 years (Allen & Seaman, 2017). Additionally, more than a quarter of higher education students (29.7 percent) in the United States have enrolled in at least one online course (Online Learning Consortium, 2017). The purpose of this study was to examine predictors of success for online learners. To this end, the researchers sought to further understand whether familiarity with and access to technology, employment status, academic readiness are predictors of grade point average and satisfactions for students enrolled in online courses.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".