MétaCan
Menu
Back to cohort

Where the reader wanders, learning follows: Promoting accessibility, equity and inclusivity in an online literature course

2024· article· en· W4405576238 on OpenAlexafffundabout
Roisin Dewart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversité du Québec à Montréal
FundersUniversité du Québec à Montréal
KeywordsPedagogyModalitiesEquity (law)Reading comprehensionReading (process)Context (archaeology)Cultural competencePsychologyComputer scienceSociologyMathematics educationLinguisticsPolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.044
GPT teacher head0.409
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same topicSecond Language Acquisition and LearningFrench-language works237,207