Bibliographic record
Abstract
The paper titled “using virtual programming lab in Web-based distance education” was presented on June 28 at EDMEDIA2012. There were about a dozen of attendees, which was better than normal for a second-last day presentation. The people in the room were all interested. One person from South California State University asked how such a system can be used in their classes, and another person from Keystone technology in the States asked a very specific question about technology used in implementing the A-VPL system, which further told me that IT firms frequently send representatives to conferences to hunt for great technical ideas. I have also attended other talks by others. It was at one of such talks I realized that I am not the only one who usually gives audience a brief introduction about AU, and hence suggested that AU should develop an official version of AU introduction for AU employees to use at such events, in order to better portrait AU to the world. The positive feedbacks from the audience further assure me the value of the system. I will seek more funding to enhance the system. A keynote speech I attended has led me to think about research in e-learning at AU. Compared to what has happened elsewhere such as in the UK, what has been missing from e-learning research are some good systems (I wouldn’t ask for any products that can be offered to others) that we can proudly show the world to convince people we have really done something.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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".