Where the reader wanders, learning follows: Promoting accessibility, equity and inclusivity in an online literature course
Bibliographic record
Abstract
Existing research on the use of literature with language learners promotes pre-reading activities to provide cultural background information before students begin reading to avoid issues of perceived inaccessibility (Lazar, 1990; Weng, 2012). More recent studies have highlighted the importance of cultural familiarity, which, when promoted with well-designed activities, has been shown to improve comprehension and retention for learners of a second or foreign language (Kuhi et al., 2013; Sheridan et al., 2019). In this project, a walking tour of a neighbourhood in Montreal, Canada, which was used as a pre-reading activity to promote familiarity with the cultural background of the text (Lazar, 1993), was adapted for a synchronous online learning context and offered using the course learning management system (LMS). Students’ experience reading the novel and their appreciation of the cultural context were positively impacted by this independent learning activity. The next stage of the project aims to enable more learners to participate by further exploring options to promote accessibility, equity, and inclusivity (Education Links, 2024) and present a dynamic experience for students who cannot explore the streets in person and make the activity appropriate for all learning modalities and varied student constraints.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".