Student Perception of a Visual Novel for Fostering Science Process Skills
Bibliographic record
Abstract
In undergraduate science education, emphasis is often placed on teaching subject matter rather than science process skills (e.g., critical thinking, problem solving). Although important to scientific training, these skills are often not taught because science educators do not feel equipped to teach them. We therefore present a case-scenario activity that aims to facilitate the development of science process skills. This activity, which takes the form of a visual novel, asks students to generate hypotheses for the seemingly odd events that are described in the story. We implemented this activity in a science-process-focused course. Upon completion of the activity, we asked students to submit a written response to the prompt: “What are you taking away from the activity?” In this exploratory study, we conducted a qualitative analysis of these written responses to ascertain whether meaningful codes and themes related to science process would arise from this open-ended prompt. Based on student responses, four main themes emerged: scientific inquiry, student satisfaction, flexibility, and collaboration. These results demonstrated the activity was both enjoyable, and it successfully enabled students to apply science process skills. We offer this activity in anticipation it will provide educators with a tool to include these skills in their classes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".