Postsecondary chemistry curricula and universal design for learning: planning for variations in learners’ abilities, needs, and interests
Bibliographic record
Abstract
Federal legislation requires equitable access to education for all students at all levels, including in the postsecondary setting. While there have been a few studies in the chemistry education research literature base focused on how to support students with specific disabilities, this work seems to exist as a separate stream of research without direct impact on curriculum development and the overall community. This study focused on investigating how well three sets of general chemistry curricular materials support variations in students’ abilities, interests, and needs. To accomplish this, we compared the curricular materials with the Universal Design for Learning (UDL) framework, which describes steps to account for variations in ability among learners during curriculum development. The UDL framework is organized into three guidelines (multiple means of representation, action and expression, and engagement), further delineated by nine principles and thirty-one finer-grained checkpoints for designing courses. We looked for examples of enactment of the UDL checkpoints in a representative sample of activities. Across all three sets of curricular materials, only four of the thirty-one checkpoints were enacted in at least 75% of the activities, indicating high enactment. On the other hand, eleven of the checkpoints were enacted in less than 25% of the activities, indicating low enactment. Overall, there is much room for improvement in consistently providing support for learner variation within these general chemistry curricular materials. We argue that some of the burden of making curricular materials supportive of all students lies with curriculum developers and provide recommendations for improving support and accessibility.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".