Emotional and Strategic Predictors of Socio-Emotional Communicative Competence Among Saudi EFL Learner
Bibliographic record
Abstract
This study examines how emotional intelligence (EI), willingness to communicate (WTC), and adaptive communication behavior (ACB) are collectively associated with socio-emotional communicative competence (SECC) among Saudi university learners enrolled in fully online English courses via the Blackboard Learning Management System. Grounded in socio-affective and communicative-competence frameworks, the research employed a convergent mixed-methods design integrating quantitative survey data and qualitative reflections from 354 first-year non-English-major students. Results revealed that SECC operates as a multidimensional construct encompassing affective, motivational, and strategic dimensions. Learners with higher EI exhibited greater WTC and more consistent use of ACB, suggesting that emotional regulation is associated with sustained communicative engagement. Although ACB contributed modestly in regression models, qualitative data emphasized its pedagogical importance for maintaining interaction and negotiating meaning. Disciplinary differences emerged, with medical and laboratory sciences students exhibiting higher EI than their peers in pure sciences or computer sciences and programming—likely due to the use of English for collaboration and the emphasis on empathy-based teamwork. Extramural English was associated with greater socio-emotional engagement, indicating that informal practice complements online instruction. These findings underscore the pedagogical necessity of integrating emotional, motivational, and strategic training into EFL curricula. Embedding socio-emotional learning within Blackboard-mediated instruction aligns with Saudi Vision 2030 educational goals by cultivating emotionally intelligent, communicatively confident, and globally competent graduates.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".