Influence of incentives on performance in a pre-college biology MOOC
Bibliographic record
Abstract
<p>There is concern that online education may widen the achievement gap between students from different socioeconomic classes. The recent discussion of integrating massive open online courses (MOOCs) into formal higher education has added fuel to this debate. In this study, factors influencing enrollment and completion in a pre-college preparatory MOOC were explored. University of California at Irvine (UCI) students of all preparation levels, defined by math Scholastic Aptitude Test (SAT) score, were invited to take a Bio Prep MOOC to help them prepare for introductory biology. Students with math SAT below 550 were offered the explicit incentive of an early change to the biology major upon successful completion of the MOOC and two additional onsite courses. Our results demonstrate that, among course registrants, a higher percentage of UCI students (&gt;60%) completed the course than non-UCI registrants from the general population (&lt;9%). Female UCI students had a greater likelihood of enrolling in the MOOC, but were not different from male students in terms of performance. University students entering with low preparation outperformed students entering who already had the credentials to become biology majors. These findings suggest that MOOCs can reach students, even those entering college with less preparation, before they enter university and have the potential to prepare them for challenging science, technology, engineering, and mathematics (STEM) courses.</p>
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".