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Record W2741943868

Creating and Developing a Student Worksheet for In-Tandem Activity

2016· article· en· W2741943868 on OpenAlexfundno aff
Alina Andreica, Ana Askar, Marius Uzoni

Bibliographic record

VenueApplied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsWorksheetSet (abstract data type)Process (computing)Order (exchange)LinguisticsForeign languageComputer sciencePedagogyMathematics educationSociologyPsychologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

Tandem activities entail communication between two partners of different cultures and mother tongues in order to use and improve their linguistic knowledge, to exchange information and to facilitate socio-cultural integration.In this respect, a set of educational materials has been created by the team of the «Tandem, bilinguisme et construction des savoirs disciplinaires: une approche du FLE/FOS en contact avec les langues de l'ECO » project.Nevertheless, all teaching material goes through successive stages, various approaches and can be improved.This study suggests an analysis of a student worksheet, Education, whose main objective is to know the educational system of the two cultures.This research mirrors two versions of the same worksheet (conventionally marked V1 and V2) in order to present the manner in which this teaching material developed.We used a comparative approach.Our points of interest were: the linguistic level of the target group, general/specific objectives, the set place and time, and the structure of the activities.The study's conclusions entailed two aspects: methodological and cultural.Thus, as far as the material's future users, we believe that it is preferable that all the students be at the threshold level of the foreign language.The activities suggested by the worksheet must make use of the participant's linguistic resources in an active process.To know the tandem partner's educational system, in order to be integrated in it, it is necessary for the general objectives to be aimed at identifying cultural similarities and differences.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.055
GPT teacher head0.375
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueApplied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca)Same topicFrench Language Learning MethodsFrench-language works237,207