Participation is Predictive of Individual, but Not Group, Work in the Context of a Blended General Education Course
Bibliographic record
Abstract
Past research on face-to-face instructional delivery demonstrates that students’ participation is positively related to their achievement in a course (Rocca, 2010), and that participation mediates the relation between attendance and achievement (Kim et al., 2019). Given that blended learning is on the rise in higher education (Johnson et al., 2016), it is of growing interest to explore whether this positive association between participation and achievement holds in the context of blended learning. Here we investigated whether students’ participation was (a) predictive of their overall grade in the course and (b) differentially predictive of their grades on three different types of assessments: tests (test and quiz), written assignments (argumentative letter and critical essay), and oral activities (debate). The results of our regression analyses showed that participation grades were predictive of learning achievement in the course with respect to overall grade (R2=0.364; ß=0.365), test grade (R2=0.164; ß==0.327), and written grade (R2=0.212; ß=0.278). Participation was not predictive of oral grades as a whole; however, further analyses showed that students’ participation predicted the individual (vs. group-based) component of the oral grade (R2=0.045; ß=0.113). Thus, our findings demonstrate that students’ participation grades are predictive of their grades on assessments that are independent but not group-based, at least in the context of the blended course investigated in this study.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".