Exploring Chemistry Professors’ Methods of Highlighting the Relevancy of Chemistry: Opportunities, Obstacles, and Suggestions to Improve Students’ Motivation in Science Classrooms
Bibliographic record
Abstract
This study focused on inquiring into undergraduate chemistry professors’ efforts in North America to increase student motivation and interest in the subject and the feasibility of methods that connect students to real world applications and societal issues related to chemistry. A survey was distributed to chemistry instructors at post-secondary institutions across the United States and Canada asking about the usage of methods and tools to deliver content aiming at raising students’ perception of the relevance of learning chemistry (N = 124). The instrument also asked about instructors’ perceptions related to assessment, as well as their perception of how their students value the integration of socio-scientific issues into the curriculum. A chi-squared analysis was performed to identify groups of individuals whose responses were disproportionate, compared to the distribution of responses from the sample, in order to identify any unique occurrences. In general, the usage of real-world applications and socio-scientific issues in post-secondary chemistry courses tends to be related to instructors’ value of the role of these topics in their courses, comfort level with the topics, and preferred approaches to developing and implementing the course materials.
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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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".