Revisiting Textbook Adaption Through Open Educational Resources: An Inquiry into Students’ Emotions
Bibliographic record
Abstract
This qualitative study explored the emotional trajectories students experienced when faced with open educational resources (OER) that expanded the learning available from a required textbook. Data included students’ reflections, group discussions, and interviews, along with field notes which were collected in a classroom at a Chinese university in one semester. The study showed that students’ initial positive emotions arose from their understanding of their own learning needs. Their positive emotions toward the conjugated use of OER and a textbook fluctuated over the semester but were gradually enhanced through their involvement in classroom practices (e.g., knowledge building and teacher mediation). Through the process, students’ positive and negative emotions respectively facilitated and hampered their learning practices; however, negative emotions were not always detrimental—they also facilitated students’ learning. Students’ emotions gradually stabilized in the direction of being positive, especially in tandem with (a) achievement of sufficient knowledge gained through OER-based textbook use and teacher-mediated learning, and (b) their augmented confidence in proficiently using the new knowledge to navigate their practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".