Connecting the Promising Practices for Teaching Culturally and Linguistically Diverse Students with Student Satisfaction
Bibliographic record
Abstract
Partially due to the increasing enrolment of international students, colleges and universities in the U.S. and Canada are becoming more culturally and linguistically diverse.According to the Canadian Bureau of International Education (2021) and the Institute of International Education (2021), more than 1.6 million international students chose to study at Canadian and American postsecondary educational institutions in 2020.Culture shock may be the first big discomfort faced by international students when they arrive in the new host country; even so, this will not be the only challenge they face.As soon as they move abroad to study, international students must adapt to new social and academic environments.Beyond living arrangements, socialization, language barriers, changes in eating practices, and in communication, international students must also face issues regarding their academic life.They will not only deal with unfamiliar methods of teaching used by their instructors, in a foreign language, but they will also have to alter their learning strategies and preferences to a new learning environment.Unfortunately, though, few instructors have received training for teaching international students (Paige & Goode, 2009; Tran, 2020), which results in a less than optimal environment for intercultural learning.Since 2020 and the outbreak of COVID-19, most students have experienced a change in the way instruction is delivered to them.It is estimated that approximately 90% of learning was online during the COVID-19 timespan (Radcliff et al., 2020).Yet, even before the pandemic, there had been a rise in the popularity of online education in North America.Online learning is increasingly being favoured by a growing range of students of various ages and diverse backgrounds, including international students.However, several gaps have been found in online teaching, including challenges faced by first-time online students, the impact of various courseloads, and learning effectiveness for additional-language students.As a result, to achieve higher-student satisfaction and perceptions of learning, instructors should analyze their roles and implement new teaching strategies to facilitate international
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".