The relation between trait flow and engagement, understanding, and grades in undergraduate lectures
Bibliographic record
Abstract
BACKGROUND: Much work has focused on inattention in the classroom, examining how episodes of task-unrelated thought (i.e., mind wandering) and engagement with various forms of media (e.g., media multitasking, smartphone use) influence retention of lecture material. However, considerably less work has examined factors that may positively influence attentiveness in lectures. AIMS: We aimed to explore whether the trait-level tendency to experience 'flow'-defined here as the subjective experience of deep and effortless concentration-is related to in-class reports of engagement and understanding during undergraduate lectures, as well as academic performance. SAMPLE: Participants were undergraduate students in Psychology at a University in Ontario, Canada. METHODS: We measured trait flow (i.e., deep, effortless concentration) at the beginning of each semester, and assessed engagement and understanding during lectures via experience sampling probes throughout two semesters in several university courses. Experience sampling probes were presented intermittently using a laptop application. We also measured students' trait mind wandering and grit, and collected students' course grades. RESULTS: The general tendency to experience deep, effortless concentration predicted engagement and understanding in lectures throughout the term, as well as final course grades, over and above students' grittiness and tendency to mind wander. CONCLUSIONS: These findings suggest that the everyday tendency to experience flow extends to a classroom environment and has implications for academic success.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".