A utility value intervention to support student engagement in online upper-level undergraduate courses
Bibliographic record
Abstract
Introduction Utility value interventions are assignments designed to increase student motivation by helping them find personal value in what they are learning. Previous studies have found utility value interventions to lead to improved student outcomes. The purpose of this study was to investigate the impact of a utility value (UV) intervention on the academic outcomes of undergraduate students in online courses, compared to students in a control condition. Methods A total of 1,243 students in upper-level online biology courses participated and were randomized either to UV (n = 589) or control (n = 654) conditions. Results The intervention had a positive effect, as UV participants engaged with and participated in their courses significantly more than students in the control condition. Utility value participants also wrote significantly longer essays, and used more pronouns, social process words, and cognitive process words than students in the control condition. However, UV participants did not have higher course grades than students in the control condition. Additionally, underrepresented minority (URM) status was a significant predictor of course grade; non-URM students had significantly higher course grades than URM students. Discussion The utility value intervention did have a positive effect on student course engagement but, contrary to most previous studies, did not lead to higher student course grades, perhaps because these classes were online, because they were upper-level instead of introductory, or because the study was conducted during the COVID-affected spring 2020 quarter. Findings from this study can help institutions decide whether to adopt UV interventions in online courses.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".