Emotional, Attitudinal, and Sociobiographical Sources of Flow in Online and In-Person EFL Classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Flow reflects an optimal balance of challenge and skill, which is exhilarating and addictive. The current study investigates the role of three learner emotions (enjoyment, anxiety, and boredom) on the proportion of class time in flow among 165 Arab and Kurdish English as a Foreign Language (EFL) students in both in-person and online classes. Statistical analyses revealed that Foreign Language Enjoyment (FLE), and more specifically, the dimension Personal FLE, was a significant positive predictor of flow, while Foreign Language Boredom was a significant negative predictor. Contrary to previous research, Foreign Language Classroom Anxiety had no significant negative effect on flow. Further analyses showed that students’ nationality and their attitudes toward English and their English teacher had significant effects on their time in flow. It thus seems that flow becomes possible when the teacher manages to get learners in the right emotional mood, allowing those who enjoy themselves intensely to rise to a state of flow, both in in-person and online classes.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it