Measuring the impact of incorporating systems thinking into general chemistry on affective components of student learning
Bibliographic record
Abstract
Recently, there has been an increased interest in incorporating systems thinking content into various chemistry classrooms. One promise of systems thinking is that students will be able to connect typical chemistry concepts learned in lectures with real-life situations through context-rich instruction. Such experiences may impact affective factors related to learning such as motivation and attitude of students. These factors have often revealed negative orientation for students in chemistry courses, where the majority of students are externally motivated, whereas intrinsic motivation is positively correlated with students’ course performance. A modified Situational Motivation Scale (SIMS) and the short version of the Attitude towards the Subject of Chemistry Inventory (ASCIv2) were used to assess whether a systems-thinking instructional approach resulted in changes in students’ motivation and attitudes in general chemistry. Pre- and post-survey data suggest that a first-semester chemistry course that incorporates systems thinking does not induce significant positive changes in students’ motivation. End of the semester motivation and attitude levels were correlated with students’ ACS exam scores, where students with higher levels of intrinsic motivation showed better performance on the ACS exam. Although the results obtained in this study were not optimistic, they suggest several areas of study within systems thinking instruction as potential areas to improve both instruction and student reception of the systems thinking components of instruction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".