Bibliographic record
Abstract
eLearning is developing at an ever increasing rate as universities and colleges recognize its vast potential to reach a deeper and fragmented student pool. For a while, eLearning was touted as the future of education, the goal we should be aiming for to answer the needs of a diversified student population with requirements predicated by the need to constantly learn new things coupled with the realities of daily life.However, as the rosy glow has faded somewhat, educators, students, and researchers alike, have raised important issues to challenge some of our assumptions as producers of on-line course content. Instructors cite a lack of time and training and concerns over security, students complain of feeling isolated and poorly stimulated by uninspiring content, while researchers question the use of "sacred" concepts such as interactivity and their real impact on the user experience.As designers and developers of on-line content for a university, we have had to address these issues to produce content that reflects, in the end, more accurately what the ultimate users feel comfortable with, but also reaches the goals set forth by faculty. This forum will focus on some of these issues and the process we went through to come up with possible solutions. We will use as an illustration of our results, an on-line course dedicated to the study of Organized Crime that we are in the process of developing and that proposes some ideas to create more appealing and enriching educational on-line content.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".