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Record W4285101474 · doi:10.2478/kbo-2022-0065

Collaborative Teaching – A Bridge Building Towards Students’ Social and Academic Benefits

2022· article· en· W4285101474 on OpenAlexaboutno aff
Daniela Duralia

Bibliographic record

VenueInternational conference KNOWLEDGE-BASED ORGANIZATION · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCraftClass (philosophy)Context (archaeology)Mathematics educationBridge (graph theory)PedagogyPsychologySociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This article is the result of an analysis based on a 14-year observation of the academic and social evolution of students in different educational institutions, contexts and levels of English and French language study in both Canada and Romania. Collaboration has proven to be an effective concept necessary to be introduced in the curriculum. The focus of this project will be on some groups of students studying English and French within different science faculties within a university in Romania. Learning crises are identified as students feel reluctant to both attend and participate in these classes. Moreover, the low number of classes in the curriculum discourages students even more. There is also an overall lack of motivation resulting in the instructor’s conclusion that a linguistic context is badly needed both inside and outside of the class. Along with their progress during their language practice, students’ motivation will be enhanced if students are involved in extra-curricular activities. Coupled with a few innovative learning strategies, the craft of teaching and social contexts presented by some native speaker guests expert on topics appealing to the students could promote greater interest in the study of these two languages.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.049
GPT teacher head0.324
Teacher spread0.276 · 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 designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueInternational conference KNOWLEDGE-BASED ORGANIZATIONSame topicSecond Language Learning and TeachingFrench-language works237,207