Impact of Open Education Resources (OER) on Student Academic Performance and Retention Rates in Undergraduate Engineering Departments
Bibliographic record
Abstract
To students and families already struggling to afford college tuition and fees, spending an additional $1,240 per year on books and supplies can be a breaking point.This cost constitutes as much as 39% of tuition and fees at a community college and 14% of tuition and fees at a fouryear public institution (data obtained from the 2019-20 College Board survey for full-time undergraduate students).Moreover, due to the coronavirus pandemic, the demand for digital textbooks is surging and the problem is compounded by the fact that without on-campus resources, including library reserve textbook collections, students are facing more barriers to access course content.Existing research also points to a negative impact on student grades, retention rates, and graduation time when there is lack of access to primary course materials.Open textbooks and open educational resources (OER) present a viable alternative to costly publisher content.Defined, open educational resources are teaching and learning materials freely available for everyone to use and are typically openly licensed to allow for re-use and modification by instructors.At New York City College of Technology -CUNY, the college's library began an OER initiative in fall 2014 to introduce faculty to OER as an alternative to traditional textbooks, and since then faculty have adopted OER across 26 of 28 academic departments and 116 courses -alleviating great financial strain and increasing access to course materials.The main objective of this paper is to investigate the association between the use of OER in engineering programs and student academic performance and retention rates.Analysis of early data demonstrates that for course sections where OER was used, retention rates increased significantly, and withdrawal rates lowered significantly.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".