What Impacts do OER Have on Students? Students Share Their Experiences with a Health Psychology OER at New York City College of Technology
Bibliographic record
Abstract
<p class="3">This article reports findings from a study conducted with students in three sections of a Health Psychology course that replaced a traditional textbook with open educational resources (OER) as the primary course material. The purpose of the study was to learn how OER impacted students. Data were collected in Fall 2015 with students from New York City College of Technology (City Tech), of the City University of New York (CUNY), a comprehensive college located in Brooklyn. Students were assigned the OER by their course instructor, who developed it as part of a library funded OER pilot initiative. Two research instruments were employed: one-on-one interviews and short surveys. Both interview and survey items asked students about how they engaged with the OER as their primary assigned course material. They shared feedback about the overall organization of the OER, ease of use, methods used to access the OER and complete coursework, benefits and challenges, and differences and similarities to using a traditional print textbook.</p><p>Findings indicate that most students were able to access the OER more easily than traditional textbooks and responded positively to the variety of learning materials and assignments the OER assembled. Most students reported that course readings were equal to or better than traditional textbooks and would be willing to register for a course offering a similar resource in the future. A small amount of students reported minor usability issues. Also, few students had difficulties obtaining technology necessary to access the OER.</p>
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".