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Record W4312116579 · doi:10.18192/olbij.v12i1.6064

Mission possible: Incorporating academic literacy and readiness into an English intensive program curriculum

2022· article· en· W4312116579 on OpenAlexaffvenueabout
Reza Farzi, Olga Fellus

Bibliographic record

VenueOLBI Journal · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCurriculumMathematics educationLiteracyPedagogyInstitutionLearning developmentEnglish for academic purposesMedical educationHigher educationSociologyComputer sciencePsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Students at Canadian universities who do not meet their program-specific language and academic requirements may be admitted to their programs with a condition to enrol in an intensive language program. While, in general, university-based language intensive programs aim to help students improve their general and academic language proficiency, our focus is to specifically enhance, foster, and render visible students’ academic literacy and academic readiness. We aim to bring forth some of the intricacies and complexities of curriculum design that are embedded within current theory and practice and address needs based nuances in teaching academic English to international students. In this article, we use a multi-dimensional framework to describe a case study of an academic institution that offers an English Intensive Program. curriculum. This program allows for, inter alia, the incorporation of multiple literacies including extra curricular activities to promote the development of a wide array of academic literacies in students enrolled in the English Intensive Program. Following a description of the theoretical framework, we discuss practical implications of including theory driven academic literacies into intensive language program curricula for different stakeholders.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.295
Teacher spread0.277 · 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 designQualitative
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
Published2022
Admission routes3
Has abstractyes

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