Bibliographic record
Abstract
This paper discusses aspects of my teaching practice, how my students perceive it, and my reflections on it. The discussion is meant to allow cross-disciplinary educators to consider adopting or adapting from my approach. Students entering the CS or Software Engineering discipline have limited prior exposure to the taught subjects. What exacerbates their learning difficulty is the class size, restricting instructor’s intentional pauses and interactions. Furthermore, there is often a gap between the theoretical insights covered in lectures and the technical pre-requisites for completing the experiential laboratory assignments. My belief in inclusive teaching has led me to support student learning and engagement through an integrated use of: 1) a drawing tablet, replacing the conventional, in-class whiteboard and mirrored to the computer desktop, for illustrating concepts and examples; 2) choreography of computer desktop activities, e.g., slide presentation, illustrations on the drawing tablet and programming IDEs; and 3) recordings accessible outside the classroom for students’ self-paced learning and review. I have acquired my experience and expertise from recording 500+ lectures and 150+ hours of tutorials, where the unifying theme is the constant and frequent use of the drawing tablet for building illustrations, from scratch, of abstract concepts and/or complex examples. In all videos, the same level of clarity, quality of presentation, and proficiency of choreographing visual annotations is maintained. As a voluntary service to my community, I designed and have been running a course designed to share my teaching experience and reflections with fellow instructors across academic disciplines.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".