Classroom Experiences of International Students in Canadian Postsecondary Education
Bibliographic record
Abstract
The aim of this paper is to examine the classroom experiences of 12 international students in a Canadian postsecondary institution. The literature related to this purpose shows that many international students are challenged by the English language proficiency needed for analytical classroom discussion, and they are often confused by Westernize student-centric learning tactics such as oral presentations and open-ended assignments. Grounded within qualitative phenomenological research, our study documents the lived classroom experiences of international students and how they interpret social learning exchanges. Based on semi-structured individual interviews, the findings reflected three main themes. First, international students faced challenges with deciphering the English language of local students and teachers. Also, they struggled with the quick response time needed for classroom discussions. Second, many international students were frustrated with mandated group work. Many students felt discriminated against by their group members; other students experienced unbalanced workloads when completing collaborative assignments. Finally, students experienced a steep learning curve regarding the Westernize student-centric approach to learning. For example, many students were intimidated by creative assignments and the associated lack of direction provided by the instructor. The findings of this research are analyzed via Bandura’s social cognitive theory, which dictates that effective learning is dependent upon personal agency (one’s actions), proxy agency (the environmental resources), and collective agency (group dynamics). Currently worldwide, burgeoning numbers of international students are streaming into Western postsecondary institutions accentuating the need for this research. These institutions have an ethical and financial obligation to supply high quality education to international students. However, to provide a supportive learning environment, instructors, administrators, and educational policymakers need to be knowledgeable of the learning experiences of international students and adjust their institutional culture and pedagogical tendencies to best suit the needs of these students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".