Online Flipped Classroom for English Enhancement Programs: A Literature Review
Bibliographic record
Abstract
This review focuses on the benefits that flipped classrooms have in English language learning programs. Relevant topics include synchronous instruction, asynchronous instruction, online courses, blended learning, and learning motivation among international students.\nTo help international students meet English language requirements, many universities in Canada offer English language enhancement programs (ELEPs) with preparatory courses international students can take before enrolling in their programs of study at their host universities. However, the outbreak of COVID-19 forced many courses to be moved from physical classrooms to online classrooms. Given that online courses may bring increased pressure and difficulties to international students (Chen et al., 2020), using appropriate learning activities by blending the asynchronous and synchronous classes may help promote the learning outcomes (Lin et al., 2019).\nIt is essential to determine which pedagogies most effectively engage and motivate students, so instructors need to conduct needs assessments and identify the best ways to meet student expectations (Henry, 2018). As a pedagogical approach, the flipped classroom can be utilized in different ways depending on students’ learning preferences and the topics taught (Singh, 2020). Based on the review of literature related to online learning for English language enhancement, particularly the benefits of the flipped classroom, this presentation aims to facilitate discussion from audience members regarding approaches that can help international students with the best possible learning experience in Canadian higher education institutions in future online courses.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".