The pedagogy of technology: Attitudes of biochemistry students towards practice questions in online game or pdf format
Bibliographic record
Abstract
In our biochemistry course we provide multiple choice questions in PDF format. Recently, we decided to use an online game. To explore possible advantages of this approach, we compared students’ attitudes towards questions in online game or PDF format. Students had access to either the PDF or the online game. Feedback was collected using a survey and analyzed by item analysis. Open‐ended comments were also solicited. The comments were grouped as either positive or negative and then categorized. The number and nature of the comments in each category was compared for the two groups. Item analysis showed that the questions were perceived as equally valuable by both groups. However, the comments revealed notable differences in attitude. The game‐users submitted a greater proportion of negative comments (71%) than PDF users (39%). The negative comments revealed that game users complained more frequently about technical difficulties, and PDF users complained more frequently about question content and errors. The positive comments revealed that both groups felt the questions helped them in exams. However, only game users reported that the format was ‘engaging and fun.’ Overall, our students valued the questions provided, regardless of format. However, those using the game and PDF formats reported quite different negative and positive attitudes, which likely impact the function of the practice questions as a learning tool.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".