But One Day Earlier: Confronting the Dragon-Tyrant in Saskatchewan Computer Science Education
Bibliographic record
Abstract
This paper examines challenges and opportunities in computer science (CSC) education in Saskatchewan, emphasizing an urgent need for reform in teacher training and professional development. The author, a self-taught CSC educator, highlights the lack of formal support and training amid rapid technological advancements, particularly in AI. An experiment demonstrates AI's potential to enhance student learning and engagement. The findings underscore the need for immediate solutions to bridge the gap between teacher practice, training, and the evolving field of CSC. Adopting Rancière's (1991) egalitarian educational philosophy, the paper advocates for educators to facilitate rather than dictate learning, leveraging AI to empower students. The proposed approach involves AI-guided, individualized instruction, promoting critical thinking and peer collaboration. The paper calls for proactive measures to adapt to technological advancements, warning that delays will further hinder the preparedness of current and future CSC educators and students and serves as a localized exploration for CSC education stakeholders in Saskatchewan, as well as providing a rationale and blueprint for a larger project.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".