Bibliographic record
Abstract
Objective: Given the current concern across the United States with improving community-college student outcomes, particularly in developmental education, understanding what students encounter inside developmental education classrooms is a necessary first step. Method: Drawing on data from a study of teaching practices inside developmental math courses at two large, urban-serving community colleges in the Northeast United States, I open up the “black box” of developmental math teaching at the community-college level. Focusing specifically on data gathered through classroom observations, instructor interviews, and curricular artifacts from six sections of developmental math, I explore two distinct curricula as they were enacted in class sessions and through the classroom discourse around solving math problems and analyze the extent to which each approach reflects the recommendations for mathematics instruction advocated by professional mathematics associations. Results: I found that differences in pedagogical goals (and related notions of mathematical proficiency) were integrally linked to differences in the what and how of assessing student learning, and that contrasting approaches to assessment maintain critical implications for accounting for failure inside developmental math classrooms. Contributions: I conclude with insights regarding future research and reform, for developmental math instruction both to realize robust mathematical learning goals and to facilitate students’ successful completion of developmental math 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".