Learning in an online distance education course: Experiences of three international students
Bibliographic record
Abstract
This case study explores the learning experiences of three international students who were enrolled in an online master’s program offered by a large university in Canada. The aim of the study was to understand the international students’ experiences with, and perspectives on, the online learning environment. Findings indicate that previous education and especially language proficiency strongly impacted the learning of these students in this environment. Non-native English speakers required considerably more time to process readings and postings and to make postings themselves. Their lack of familiarity with the details of North American culture and colloquial language made it difficult to follow much of the course discussion. They also tended to avoid socializing in the course, which left them at the periphery of course activities. Based on these findings, the authors make the following recommendations for designers and instructors of online courses: 1) Raise the English language proficiency requirement for graduate admissions into online programs because the text-based communication in a CMC space requires interpreting messages without non-verbal cues; 2) Ensure that online distance education course designers are aware of the needs and expectations of international students; and 3) Combine the design principles from both traditional and constructivism theories.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".