Chinese International Graduate Students’ Experience of Engagement in Online Learning in Canadian Higher Education: An Ecological Perspective
Bibliographic record
Abstract
While the last decade has seen a dramatic increase in both the acceptance and use of online learning in post-secondary education (Seaman & Seaman, 2016), studies have documented both higher and lower student engagement in online learning (Muthuprasad et al., 2021). Most previous studies on online learning focused on a solitary dimension of student engagement in their analytical models (Pianta et al., 2012), rather than embracing a more comprehensive sociocultural framework. Using a narrative inquiry methodology, I examined the online learning experience of six mainland Chinese international graduate students who had at least one-semester experience of online learning in Canadian universities. Specifically, this study explored: (a) the students’ online engagement experiences; (b) factors that influenced their online learning; and (c) the role of cultural factors that influenced their online engagement. The data for the study were drawn from semi-structured one-on-one interviews and the analysis of written narratives provided by the participants. Bronfenbrenner’s ecological model served as the theoretical framework for this study, facilitating the examination of various dimensions of students’ engagement in online learning. The findings indicated that participants generally held positive perceptions of their online learning experiences, while also recognizing the inherent advantages and challenges associated with this mode of education. The study revealed that student engagement was molded by a complex interplay of factors, such as personal interests, motivation, course attributes, instructor effectiveness, technological tools, visual aids, language considerations, peer interactions, familial and social connections, and the broader learning environment. The opinions of the participants varied on commonly held beliefs about Chinese culture influencing them, with some acknowledging these preconceived notions and corresponding behaviours aligned with them, while others challenged these stereotypes and emphasized individual differences and cultural contexts. The students’ accounts also highlighted aspects of Canadian culture with regard to equality, freedom of expression, independence in education, and diverse teaching styles among professors. Applying Bronfenbrenner’s ecological system theory revealed the interconnectedness of factors at different levels. With an understanding of these dynamics, I discussed implications of the findings in conceptualizing culturally responsive teaching practices, enhanced engagement and success among Chinese international students in online learning.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".