Widening university participation in learning using students’ contextualised storytelling in general chemistry
Bibliographic record
Abstract
Many students find introductory general chemistry courses difficult because they feel alienated by traditional approaches to teaching and learning. This can become particularly problematic in laboratory sessions where students simply follow processes and procedures that students can view as being mundane and lacking creativity. Contextualised storytelling offers a novel pedagogical approach to help students connect and make sense of chemistry ideas in the context of their own life experiences. The current study implemented the CLEAR (chemistry learning via experiential academic reflection) approach to contextualised storytelling as a sequence of four assignments across a laboratory course for first-year students. The research explored students’ experiences writing contextualised stories to make sense of and learn chemistry. Using hermeneutics as a methodology, the data collected included participants’ written contextualised stories, semi-structured interview recordings, and field notes. While the CLEAR approach differs from other approaches to storytelling in chemistry education, the current study suggests that CLEAR can make positive contributions to student learning. The findings showed that, although many students initially resisted or felt confused by the new approach, CLEAR helped students see the connection and relevance of chemistry concepts to their lives. Students also recognized the importance of self-directed learning while writing their CLEAR stories, which suggests that CLEAR engaged students in learning that was active and organic. Furthermore, writing CLEAR stories supported students in talking to people about scientific concepts they learned in class, which suggests that writing the CLEAR stories: (a) helped students find the relevancy of the ideas to the degree that they felt they could share the ideas in their own words outside of class and (b) increased students’ interest in the course and what they were learning to the degree that they wanted to share it. Implementing CLEAR as multiple assignments across the course appears important and valuable because students refined their thinking and writing skills through iteration within and across CLEAR stories.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".