Assessment of Academic ESL Writing in an Online Tutorial for Graduate Students
Bibliographic record
Abstract
English-as-a-second language (L2) graduate students often face challenges in developing academic writing skills, which can be exacerbated by a lack of timely support at institutional level. To help address this concern, an original set of online academic writing tutorials with a focus on the genre of Literature Review was designed by the authors to assist international graduate students enrolled in graduate programs in Canada. This paper introduces and compares multiple assessment tools employed in the online tutorial set for international English L2 graduate students. Our project pursues two major goals: First, to address the above-identified gap in Academic Writing support to English L2 graduate students with minimal costs and faculty involvement through designing the online tutorial set; second, to contribute to the research on e-learning of Academic English as a second language (ESL) writing in terms of developing integrated tools for online tutorial building, analysis of texts produced by learners, and assessment of learners’ writing progress. The research questions are: 1. What resources can be combined to develop an online tutorial set for graduate students at minimal costs? 2. How can the learners’ progress in academic ESL writing be assessed with different assessment tools? First, the paper describes the tools employed in the tutorial construction: MoodleCloud platform, H5P interactive e-book designed by the authors, surveys, and assessment tools. Second, we present and compare assessment tools employed to evaluate learners’ writing progress: Expert assessment with an analytic rubric, self-assessments of progress by the participants, and automated text analysis with corpus-based tools as reported in Li, Makarova, and Wang (2023). A comparison of the scores across the three assessment tools shows some discrepancies, which seems to suggest that combined tools yield a more comprehensive picture. The expert assessments and self-assessments demonstrate improvement in the writing quality over the course of the tutorial series, which are partially supported with the findings from corpus-based analysis of participants’ texts. The findings are of relevance to e-learning scholars, faculty, and administrators of English-medium universities with substantial intakes of international graduate students in Social Sciences and Education whose native languages are other than English.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".