The Effects of Teachers' Flow Experiences on the Cognitive Engagement of Students
Bibliographic record
Abstract
The purpose of this quantitative study was to investigate the relationship between teachers' flow experiences and student cognitive engagement during class time. Study participants consisted of students and their teachers in grades 6 through 10 from a number of elementary and secondary schools in the Fraser Valley region of British Columbia, Canada. Data were collected through the use of the Experience-Sampling Method, originally developed by Csikszentmihalyi to investigate flow experiences, that allowed teachers and students to fill out sampling forms in response to randomly generated electronic signals transmitted to pagers or wrist watches that the teachers wore. Data were collected from a total of 190 classes, generating 5047 individual observations on students cognitive engagement. Multiple regression analysis was then used to address the following two research questions: 1. Are there differences in the cognitive engagement of students when their teachers are experiencing flow? 2. To what extent are these differences in cognitive engagement influenced by grade level, subject matter, time-of-day, instructional method, and the gender composition of the class and instructor? Findings from this study suggest that a strong statistical relationship exists between teachers' flow experiences and the cognitive engagement of their students. Specifically, when teachers were experiencing flow, 25 percent more of the students in class were cognitively engaged than when teachers were not experiencing flow. Results from the regression analysis also indicate that grade level, subject matter, and the gender composition of the class were also significant determinants of the cognitive engagement of students. Taken together, the findings from this study demonstrate the importance of the psychological connection between teachers and their students in the classroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".