What Can You Do To Virtually Teach Hands-on Skills?
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
What can you do to virtually teach the hands-on skills traditionally taught in labs? If you include simulations, active experimentation, discovery-learning techniques, numerous questions with detailed feedback, video, animations, and photographs, you can effectively teach practical hands-on skills through multimedia technology. Through discussion and demonstration, this session will highlight practical tips for implementing the instructional development cycle as well as uncommon but effective instructional design strategies for teaching practical skills. Some of the highlighted programs (such as a virtual chemistry lab) have pushed the boundaries of what can be accomplished with multimedia technology. By the end of this interactive session, participants (who can range from novices to experts) should be able to identify computer-based training applications that effectively use multimedia technology, generate examples of where new media technology can be appropriately used to virtually teach hands-on skills, and decide where to incorporate the strategies shown into their computer-based training productions.
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 | 0.001 | 0.001 |
| 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