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Record W3199421865

Introducing a Language Portfolio to Motivate High School Students

2005· article· en· W3199421865 on OpenAlexaboutno aff
E. Di. Benedetto

Bibliographic record

VenueLe Simplegadi · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsLearner autonomyPortfolioForeign languageLanguage educationLanguage assessmentPedagogyMetacognitionLanguage acquisitionVocational educationPsychologyMathematics educationSociologyComprehension approachCognition
DOInot available

Abstract

fetched live from OpenAlex

The European Language Portfolio is one of the most innovative tools developed by the Council of Europe to implement its policies aimed at promoting plurilingualism and pluriculturalism. Based on the latest developments in metacognitive strategies for learning, it provides useful assistance to language teaching and learning. This article analyses the introduction of a language portfolio into two secondary school first forms, as part of a teaching practice experience in a vocational school. Focusing on the phase of self- assessment and its impact on motivation to learn, it explores possible ways to introduce students who are not used to reflecting on their learning to metacognition. Bibliography Bachman, L.F. and Palmer, A.S. 1989.  The construct validation of self-ratings of communicative language ability . Language Testing 6/2: 14-25. Blue, G. 1994. Self-assessment of foreign language skills: Does it work?  CLE Working Papers 3: 18-35. Brindley, G. 2001. Assessment. In R. Carter and D. Nunan (eds.)  Teaching English to Speakers of Other Languages . Cambridge: Cambridge University Press. Pages: 137-143. Dickinson, L. 1987.  Self-Instruction in Language Learning . Cambridge: Cambridge University Press. Dornyei, Z. 2001.  Motivational Strategies in the Language Classroom . Cambridge: Cambridge University Press. Heron, J. 1988. Assessment revisited. In D. Boud (ed.)  Developing Student Autonomy in Learning . New York: Kogan. Pages: 77-90. Mariani, L. and Tomai, P. 2004.  Il portfolio delle lingue. Metodologie, proposte, esperienze . Roma: Carocci editore. Oscarson, M. 1997. Self-assessment of foreign and second language proficiency. In C. Clapham and D. Corson (eds.)  Encyclopedia of Language and Education, Vol. 7: Language Testing and Assessment . Dordrecht: Kluwer Academic. Pages: 175-187. Ready-Morfitt, D. 1991.  The role and limitations of self-assessment in testing and research . Unpublished manuscript, Second Language Institute, University of Ottawa. Ross, S. 1998.  Self-assessment in language testing. A meta-analysis and analysis of experiential factors . Language Testing 15/1: 1-20. Council of Europe. 2000.  European Language Portfolio (ELP): Principles and Guidelines . Downloaded from the website  www.coe.int/portfolio on 21/04/05. Language Policy Division. 2004.  European Language Portfolio (ELP). Principles and Guidelines with added explanatory notes (version 1.0) . Downloaded from the website  www.coe.int/portfolio on 21/04/05. Little, D. and Perclova, R. 2001.  The European Language Portfolio: a guide for teachers and teacher trainers . Downloaded from the website  www.coe.int/portfolio on 21/04/05. Oxford, R. 1989.  The Role of Styles and Strategies in Second Language Learning . Eric Digest. Downloaded from the web address  http://www.ericdigests.org/pre-9214/styles.htm on 22/04/05. Oxford, R. 1994.  Language Learning Strategies: An Update . Downloaded from the web address  http://www.cal.org/resources/digest/oxford01.html on 22/04/05. Peirce, W. 2003.  Metacognition: Study Strategies, Monitoring, and Motivation . Downloaded from the web address  http://academic.pg.cc.md.us/~wpeirce/MCCCTR/metacognition.htm on 13/04/05.

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)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.015
GPT teacher head0.396
Teacher spread0.381 · 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
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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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