Unravelling Students’ Choice of Course Modality and Flow Experience in Multi-Access University Courses in Relation to Interpersonal Personality Traits
Bibliographic record
Abstract
Recent developments have led institutions into a transition towards more flexible educational models, such as synchronous hybrid education, i.e., having both on-site and remote students at the same time or multi-access education also providing asynchronous access. It is assumed that these new models can enhance accessibility for learners with diverse learning needs, creating opportunities for inclusive education. However, prior research investigating the relationship between student choice of course modality and personality, and the effect of this choice of delivery mode on affective learning outcomes remains underexplored. The current study filled this gap by exploring student choice behaviour regarding course modality, examining the influence of interpersonal personality traits on these choices, and assessing the impact of course modality on students’ flow experiences. This research builds upon prior research on synchronous hybrid education, digital personalized learning, personality, and flow. Our study shows evidence for the significant relation between degree of introversion–extraversion and the choice of course modality, with more introverted students tending to prefer the remote setting compared to the on-campus setting. Moreover, the findings confirmed the influence of course modality on flow experiences. In this respect, our study contributes to the research on personalized learning by showing that current technological evolutions provide choices about where students learn, in addition to what and how they learn. This creates a new dimension of adaptivity, opening possibilities for inclusive education, yet also adding new challenges.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".