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
This research investigated the effects of applying intelligent agent techniques to an online learning environment. The knowbots (or Knowledge Robots) created for the research were intelligent software agents that automated the repetitive tasks of human facilitators in a series of online workshops. The study specifically captured experimental results of using knowbots in multiple sessions of an ALN (Asynchronous Learning Network) online workshop, Getting Started Creating Online Courses. The study used experimental groups and comparison groups to examine the association between the use of knowbots and workshop completion rates. Also examined were the effects of knowbots on other factors such as facilitation time and learner satisfaction. The findings indicated that the use of knowbots was positively associated with higher learner completion rates in the workshops. In addition, knowbots implemented a learning-support tool that reminded learners about deadlines. The support knowbots were found to be effective autonomous motivators. In sum, the results of this research suggest that the application of agent technology to online learning holds promise for improving completion rates, learner satisfaction, and motivation.
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 | 0.000 | 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 it