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Record W2290116403 · doi:10.18192/olbiwp.v5i0.1126

Collaborative Italian: Using technology to support student-led language teaching

2013· article· en· W2290116403 on OpenAlexvenueno aff
Cecilia Goria

Bibliographic record

VenueOLBI Journal · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningOpenness to experienceCollaborative learningLanguage acquisitionSocial mediaEducational technologyPedagogySocial constructivismConstructivism (international relations)Cooperative learningComputer scienceMathematics educationOnline discussionPsychologyTeaching methodWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

Collaborative Italian—Collit—is an online language learning program for adult students of Italian in higher education with at least a B1 level of the Common European Framework. Collit is free, open and extracurriculum. Collit provides the learners with a communicative learning experience based on collaboration and social interaction. It promotes social constructivism and embraces Web 2.0 pedagogies by relying on the openness of the online environment and social media. Collit emphasises ownership of learning by encouraging student-controlled intended learning outcomes and tasks, and student-generated learning content, consistent with the cognitive and the experiential approaches to course design onto which Collit is theoretically grounded.On this basis, Collit addresses questions concerned with the effectiveness of open pedagogies for student-led language learning. This contribution presents Collit as a case study for the investigation of novel language learning dynamics supported by the web and social media.

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 categoriesInsufficient 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 score0.099
Threshold uncertainty score0.989

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.316
Teacher spread0.304 · 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
Published2013
Admission routes1
Has abstractyes

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