Open Educational Resources and Student Course Outcomes: A Multilevel Analysis
Bibliographic record
Abstract
Salt Lake Community College (SLCC) is Utah’s largest open enrollment college, and as an institution, is concerned about the expense associated with attaining a degree. All students face challenges in paying for their education, but SLCC students tend to have fewer resources to dedicate to school than students at other institutions in the state. While faculty and administrators have little control over the rising cost of tuition, they are able to offer students open educational resources (OER) to cut down on textbook costs. Salt Lake Community College’s OER initiative was implemented in Summer 2014, and has since expanded to include 125 sections in Spring 2016. We examine OER’s impact on three measures of student success: course grade, likelihood of passing, and likelihood of withdrawing. We use a multilevel modeling (MLM) approach in order to control for student, instructor, and course effects, and found no difference between courses using OER and traditional textbooks for continuing students. For new students, there is evidence that OER increases average grade. However, student-level differences such as demographic background and educational experience have a far greater impact on course grade and likelihood of passing or withdrawing than an instructor’s use of an OER text. Future research should focus on longer-term impacts of OER on retention, completion, and transfer.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".