Developing Transversal Competencies through Blended Learning: A Phenomenological Study of Thai Undergraduate Students
Bibliographic record
Abstract
This phenomenological study explored undergraduate students’ lived experiences of developing transversal competencies (TVCs) through blended learning in an English for Specific Purposes (ESP) course. Thai higher education institutions have implemented blended learning approaches in classrooms. Moreover, the institutions have developed competency-based curriculum to prepare students for labor markets. Moreover, the benefits and challenges in developing TVCs in a blended classroom were investigated. Data were collected from 17 Sports Science and Health students who registered for the “English for Employment” course. The blended online classroom combined synchronous and asynchronous sessions. Data included classroom observations, focus group interviews, and student reflective journals. The data were analyzed using Moustakas (1994)’s phenomenological analysis framework. Phenomenological study was well-suited to the research questions, as it aimed to capture the understandings of students’ lived experiences in authentic contexts. This approach allows for rich understandings of how TVCs develop in a blended learning context. The analysis demonstrated the essence of a phenomenon that included TVCs as a learning journey, different modes of learning TVCs, and teacher and peer influence. The learning journey of the participants started with their unfamiliarity with the term TVCs, but they gradually recognized its importance for employability. The participants experienced TVCs through three modes of learning: interactive instruction, independent study, and direct instruction. The benefits included enhancing students’ motivation, autonomous learning development, and technological practice. However, low student motivation, technological barriers, and ethical issues emerged as challenges. The findings illustrated the importance of teacher support in facilitating students’ development of TVCs in blended environments in ESP courses, which contributed to the understanding of competency development in Thai higher education contexts. However, it was limited to a specific group of participants. This study was collected from a small sample size of 17 participants from one faculty and depended mainly on a qualitative approach.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".