Breaking barriers : understanding and removing barriers to OER use
Bibliographic record
Abstract
While there are many benefits to the use of OER, such as cost savings for students, increased access to resources, and the ability for faculty to adapt the resources to meet their specific needs, new and experienced faculty members also face many barriers when attempting to incorporate Open Educational Resources (OER) into their courses. Research suggests that awareness, funding, time, and institutional supports are factors that impact faculty using or not using OER. The purpose of this research was to investigate the barriers that business faculty in Ontario colleges face when using OER within their teaching practices and determine if faculty have recommendations to overcome the barriers to using OER. Based on a review of the literature on OER and the barriers business faculty experience when using OER, a mixed-method approach was used in this research. The study focused on Ontario college faculty teaching business courses. Data was collected via a survey and follow-up interviews. Seventy-two respondents from 12 Ontario colleges responded to the survey. Nine participated in follow-up interviews. Respondents were asked about their experiences using OER, the barriers they faced, and solutions to overcome them. A thematic and cross tabulation analysis of the responses demonstrated that faculty are introduced to OER in different ways, and institutions have unique approaches to supporting faculty with OER. Faculty experience barriers to using OER, such as no suitable resources, awareness, knowledge, support, and institutional processes. Faculty outlined ways to overcome such barriers, including but not limited to professional development, creation of new high-quality content, time to create the resources, and enhanced collaboration and networking efforts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".