Bibliographic record
Abstract
Asynchronous Learning Environment (ALE) has the capability of providing learning to people anywhere and at any time for both to secure degree and to engage in continuing education throughout their lifetimes. The advance of communications and information technology will make students choose to purchase and enroll in open market, widely available networked courses regardless of institutional affiliation. Research results have found that success factors for asynchronous learning include whether students felt part of the online learning group, immediate feedback from instructors, automatic self-test, and indicating student's performance and progress in the course. These findings present basic requirement for the design of ALE. This paper explores all aspects of Asynchronous Learning Environment, including the architecture of ALE and complete database design. The modules of ALE include multimedia presentation, identity verification, intelligent agent, automatic test marking, computer conference, chat & whiteboard, and learning scheduling assistance. The purpose of this research is to make ALE a better way of education than traditional education. A database is designed to fully support these ALE functions. Guidelines of designing ALE are provided with implementation examples of intelligent agents that providing automatic reminders and learning progress report. Conclusion and further works are discussed at the end of the paper. The design described in this paper is intended for use by engineering courses. But it can be used by courses of other disciplines without much modification.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".