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Record W2156399447 · doi:10.5430/ijhe.v2n2p1

Coping with English as Language of Instruction in Higher Education in Rwanda

2013· article· en· W2156399447 on OpenAlexvenueno aff
Kagwesage Anne Marie

Bibliographic record

VenueInternational Journal of Higher Education · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersStyrelsen för Internationellt Utvecklingssamarbete
KeywordsTranslanguagingCode-switchingMedium of instructionCoping (psychology)Foreign languagePsychologyEnglish for academic purposesEnglish as a foreign languageMathematics educationPedagogyLinguistics

Abstract

fetched live from OpenAlex

The present study examines strategies that multilingual university students in Rwanda use in order to successfully deal with complex academic material offered through the medium of English, a foreign language. The reported strategies emerged from group work discussions and interviews with students in the faculty of Economics and Management at a University in Rwanda. The data were analyzed thematically. Findings reveal that students have multiple coping strategies that enable them to complete academic tasks given through the medium of English. One of the prominent strategies and practices is the successful use of other languages at their disposal to mediate cognitively demanding academic tasks. Although other spoken languages are not officially recognised as media of instruction in higher education, they play a mediating role in content learning through responsible code switching and translanguaging.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.012
GPT teacher head0.275
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations145
Published2013
Admission routes1
Has abstractyes

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