Bibliographic record
Abstract
Some courses are just not of interest to some students but are nonetheless important to their education as engineers. First-year chemistry for engineers has been shown to be one such course; student exit surveys at graduation indicate that students from some programs didn’t see how the course connected to their program or into their careers as engineers [1]. Gamification is a popular technique where game elements, like point systems, are integrated into a course structure to motivate student learning and participation [2]. In the offering of this first-year chemistry course in the summer of 2019, the instructor applied gamification techniques in the course design. The major changes to the course structure included the ability to “unlock” an alternate grading scheme if at least five of seven tasks were completed, at least three of which were the completion of a “Six Degrees of Chem & Bacon” challenge. These challenges required the students to think about a core topic in the course module and find a practical application from their discipline of engineering. The experiences of the instructor and a student in the Spring 2019 class are shared here. The big take-aways are 1) to consider the benefits of reflection in learning, and 2) to consider the workload, both of the students and of the teaching team, when adding this type of activity to a course.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.394 | 0.141 |
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".