Factors Affecting Iraqi Academic Leaders' Communicative Competence in English: A Sequential Mixed Methods Study
Bibliographic record
Abstract
English language communicative competence represents an academic leader's ability to use language effectively. However, to date, Iraqi academic leaders’ communicative competence has not been as effective as predictable and leads to lower intake of international students in Iraqi universities. This paper presents the findings of a study which explored the factors affecting Iraqi academic leaders’ communicative competence in English. The study adopted an exploratory sequential design, where nine semi-structured interviews were conducted online with academic leaders, followed by questionnaire distribution to 108 additional academic leaders. The participants for both parts of the study were from various universities in Iraq. The data collection and data analysis for the research were guided by four theories, namely the communicative competence theory, communication theory, theory of planned behaviour and leadership competence model. Data collected from the interviews were transcribed and analysed thematically. Data from the questionnaires were analysed descriptively and inferentially using SPSS (v.20). The most significant findings were that cultural factors, a positive attitude towards communicative ability, efficiency in communication, collaboration, problem-solving, and critical skills were the factors that affected the Iraqi leaders’ communicative competence. Finally, a model for developing communicative competence among Iraqi academic leaders was formulated based on the findings. The model consists of training programmes for communicative competence development using digital tools for language use, an electronic examination process with efficient monitoring, and language initiatives for enhancing Iraqi academic leaders' communication competence.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".