Bibliographic record
Abstract
Higher education is engulfed in change. At the same time that institutions of higher education are endeavoring to transform themselves by integrating information and communication technologies into curriculum delivery, student profiles are changing. Low income-ethnic populations are among the fastest growing segment of 18–24 year old students; male enrollments are lagging in comparison to female; and the “digital natives” have arrived. Also, as the Internet provides students with access to a myriad of global educational opportunities, the potential for serving virtual foreign students increases. These changes present challenges and opportunities to institutions of higher education, which strive to serve their constituents through fully online and blended learning formats and aspire to extend education to new markets as well. This paper raises implications for online learning related to changing student populations. It presents two fundamentals crucial for ensuring student success, as well as, access in an online environment. Finally, it recommends two change strategies.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".