Doing the Research that Informs Practice: A Retrospective View of One Group's Attempt to Study The Teaching and Learning of Organic Chemistry
Bibliographic record
Abstract
The idea that the focus of educational research should be on results that can inform the practice of teaching has been an implicit assumption for so many years that one would be hard-pressed to trace it back to an individual source. At one time, the people doing such research in STEM disciplines were faculty in schools or colleges of education who focused on K-12 classrooms and looked for ideas, concepts, and principles that would be valid across a range of STEM disciplines. Eventually, this research was done on college- or university-level students, as well, and there was a shift toward what has been called discipline-based educational research (DBER) that looks at the problems associated with the teaching and learning of a given discipline, such as chemistry. This paper will discuss the results of research on problem-solving in chemistry that has been done in our research group, with particular emphasis on the challenges of teaching and learning organic chemistry. The goal of this paper is to show what can happen when one listens carefully to students and begins to appreciate the difference between what we think we have taught and what the students learned. The examples we will use have the potential for convincing those of us who teach chemistry to rethink what we do in our classes to find better ways of helping our students understand the material we are trying to teach. Although this paper will focus on results from the second-year organic chemistry course, similar results have been observed in both inorganic and physical chemistry, as well as biochemistry courses.
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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 | 0.053 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".