Mediating knowledge through peer-to-peer interaction in a multicultural online learning environment: A case of international students in the US
Bibliographic record
Abstract
The continuous growth of online learning and its movement towards cross-border and cross-culture education has recently taken a new turn with the epic hype that currently surrounds the development of massive open online courses (MOOCs) (Beattie-Moss, 2013). This development brings to focus the experiences of international students who take online courses designed and offered within the paradigm of Western pedagogy. Employing a sociocultural theoretical framework (Vygotsky, 1978; Scollon & Scollon, 2001), this paper examines the mediating roles that peers may play in the context of multicultural online learning environments. This two-stage, mixed methods study explored the experiences of 12 international graduate students who took fully online courses in a large research university in the northeastern region of the United States. The data included a survey, online interviews, as well as a case study that took a close look at the experiences of a female student from China. Findings of the study demonstrated that international students that come from diverse native academic backgrounds and cultures may necessitate a close relationship with peers they meet in the US courses. Peers become invaluable mediators of knowledge for international students who seek peer assistance to compensate for the lack of culture-specific knowledge and skills and to satisfy their interest in the host culture. The study suggests that course developers and facilitators should be proactive when assigning group projects and activities so as to enable close peer-to-peer interaction and opportunities for building personal relationships with other class members.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".