Assignment resubmission and the emergence of a third space: International students’ discursive negotiation tools
Bibliographic record
Abstract
This paper utilizes Gutierrez’s (2008) conceptual framework of the third space, the cultural-historical activity theory (CHAT) (Engerström, 2015), along with Bakhtin’s (1981) dialogical theory, to identify the discursive negotiation tools employed by international students at a private Canadian university when requesting the opportunity to resubmit their assignments. While this practice is not formally regulated by the institution, students frequently make these requests, supporting them with a range of arguments. During the two phases of this study, a total of 4,018 emails were collected, which are analyzed using Torres-Arends’ (2023) analytic framework to explore the reasons behind students’ requests for resubmission. The findings revealed that a third space emerged through students’ negotiation strategies in response to challenges such as meeting assignment requirements, balancing multiple responsibilities, and navigating a new academic culture. Based on these findings, two recommendations are proposed: first, that this “emergent” third space be recognized as an opportunity to redesign class materials, adjust pedagogic practices, and implement responsive course designs in culturally diverse learning environments, and second, that further research be conducted to explore professors’ perspectives on accepting or denying requests for assignment resubmission, adding a critical dimension to understanding this negotiation process.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".