The Impact of Emotions on Student Achievement in Synchronous Hybrid Business and Public Administration Programs: A Longitudinal Test of Control‐Value Theory*
Bibliographic record
Abstract
ABSTRACT Synchronous hybrid delivery (simultaneously teaching on‐campus and online students using Web conferencing) is becoming more common in higher education. However, little is known about students’ emotions in these environments. Although often overlooked, emotions are fundamental antecedents of success. This study longitudinally examined the role of students’ emotions (enjoyment, anxiety, and boredom), perceptions of control, value, and success in synchronous hybrid learning environments. In particular, the investigation assessed students’ self‐reported enjoyment, anxiety, and boredom as predictors of their program achievement and successful technology use. Students were recruited from synchronous hybrid MBA and MPA programs. Control‐value theory of emotions was used as the theoretical framework. Paired samples t‐tests revealed that the achievement domain, compared to the technology domain, yielded higher mean scores for control, value, enjoyment, anxiety, and boredom. In addition, mixed ANOVAs indicated an interaction effect in which group means for program boredom were significantly higher for on‐campus students than for online students. Intercorrelations in each domain showed that perceived success was positively related to enjoyment and negatively related to anxiety and boredom. Technology‐related anxiety was also found to fully mediate the positive effect of control on perceived success in using technology.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".