Bibliographic record
Abstract
The requirement for a method of capturing problem solving on a whiteboard for later replay stems from my teaching load, which includes two classes of first-year university general physics, each with relatively large class sizes of approximately 80–100 students. Most university-level teachers value one-to-one interaction with the students and find working out problems on a board a useful teaching method. However, in most institutions of higher education, the staff-to-student ratio precludes giving every student this learning experience. The syllabus of the algebra-based physics course at the University of Saskatchewan (Physics 111) is relatively ambitious in terms of the content covered, given the physics and mathematics background knowledge of the average student. This means that the number of problems worked on in class is rather limited if a thorough discussion of the basic principles is required. Some form of tutorial that records the essence of working out a problem on a board, with both visual and audio elements and which can be replayed over the Internet, is desirable. Obviously, this loses the interactive question-and-answer element possible in a true tutorial where the student and teacher are both physically present, but it does have the significant advantage that the tutorial can be replayed as many times as the student deems it necessary, thus allowing the lesson to proceed at a pace dictated by the student. Moreover, these lessons only have to be prepared once, can be used many times over, and can be used in distance-learning courses. In this paper, I describe the necessary hardware and software required to do this, all of which is relatively affordable and requires little specialist IT knowledge to set up.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.036 |
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".