Work In Progress: Reversi: A Platform for Teaching Programming Languages
Bibliographic record
Abstract
This paper introduces a new set of infrastructures and online interactive tools that can be employed to motivate students to learn programming languages.The tools were used to experiment in one of the introductory first-year engineering courses.The final project of the course requires implementing an AI program for a game called "Reversi''.Reversi is a medium to hard level programming project that has been used in the course for several years requiring an immediate restructuring.Furthermore, due to COVID-19 and the restriction of in-person teaching, it has been a challenge for educators to excite, support, and encourage students.The new infrastructure provided an interactive platform for the students to familiarize themselves with the Reversi game project.It also provided a leaderboard, an interactive scoreboard, allowing students to compete with their classmates.The tools can instantaneously synchronize to students' code submission to help students check their latest ranking among their classmates in real-time.This increased students' level of engagement and learning.In addition, it allowed students to collaborate with their fellow classmates and discuss their algorithms.The tools and platform developed can also be employed in other courses as well other programming games.The result from students' surveys and the active trend of the class online discussion forum indicates that the new online interactive system created a positive atmosphere and increased students' motivation in learning programming languages.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.026 |
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".