Integrating MOOCs in Formal Education: To Unveil EFL University Students’ Self-Learning in Terms of English Proficiency and Intercultural Communicative Competence
Bibliographic record
Abstract
This study employed a blended learning approach to investigate 98 medical university EFL students’ perceptions and analyzed the learning trajectory of their LMOOCs in formal education. Meanwhile, Byram’s (1997) intercultural communicative competence (ICC) model was adopted to explore if students’ English proficiency and ICC abilities could enhance or hinder their LMOOCs. Participants of this elective two-credit course, “English Presentation Skills,” were all required to complete one LMOOCs course to earn official certificates for the credits. Questionnaires about self-learning background and intercultural communicative competence, weekly reflections on self-learning, assistant-student interviews, and personal presentations at the final stage are conducted to test the consistency between student self-evaluation and class performance. The rubric of the TOEFL iBT Test was adopted to evaluate students’ oral expression. Video clips collected in class were analyzed to track the differences in students’ target skills. Findings revealed that before integrating LMOOCs in formal education, it is essential for instructors to equip students with enough self-learning skills through orientations and stress time management in MOOCs learning strategies, and scaffold sufficient English communication skills before students are thrown to sink or swim on their won. A revised version of the ICC model was proposed at the end (the context may vary) in the hope of drawing more attention and voice to this research area. The integration of LMOOCs in formal education, be it for language teaching or self-learning, should be directed toward the level of “precision instruction” in the future.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".