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Record W4295309486 · doi:10.20343/teachlearninqu.10.32

Student Perception of a Visual Novel for Fostering Science Process Skills

2022· article· en· W4295309486 on OpenAlexafffund
Michael Wong, Ahmed Al-Arnawoot, Katrina Hass

Bibliographic record

VenueTeaching & Learning Inquiry The ISSOTL Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of OttawaUniversity of TorontoMcMaster University
FundersMcMaster University
KeywordsAnticipation (artificial intelligence)PerceptionFlexibility (engineering)Process (computing)Mathematics educationPsychologyScience educationSubject matterComputer sciencePedagogyCurriculum

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.484
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

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