Student Voice in Textbook Evaluation: Comparing OpStudent Voice in Textbook Evaluation: Comparing Open and Restricted Textbooksen and Restricted Textbooks
Bibliographic record
Abstract
Advocates for student voice in higher education believe students should have the right and power to engage in much of the decision-making traditionally dominated by instructors or administrators. This qualitative study examines the role of student voice in the evaluation of textbook quality. Evaluators included two graduate students enrolled in a project management course at a university in the western U.S. and their instructor. Evaluators used their own student-created metric to analyze the comparative quality of eight graduate-level project management textbooks, two of which were open and six copyright-restricted. The purposes of this study were to assess the comparative quality of low-cost open textbooks and traditional copyright-restricted textbooks and to identify key student-generated criteria wherein all textbooks may be improved to better serve student needs. The analysis revealed that textbooks can be rigorously and meaningfully evaluated by students, that open textbooks can compete with restricted textbooks in terms of quality, and that polyphonic approaches to textbook evaluation can be valuable for learning. We discuss the implications of open textbooks as viable, high-quality classroom options, and the importance of valuing both student voice and instructor perspectives to ensure the highest quality textbook selection for courses in higher education.
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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 | 0.044 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".