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Record W2765432596 · doi:10.5539/jedp.v7n2p124

Students’Attitudes towards Foreign Languages

2017· article· en· W2765432596 on OpenAlexvenueno aff
Mirjana Matea Kovač, Ana Mrsic

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsGermanStatement (logic)Foreign languagePsychologyLinguisticsMathematics education

Abstract

fetched live from OpenAlex

This paper analyses similarities and differences in attitudes and beliefs towards learning the German and the Italian language. The testing tool used for evaluating the attitudes is a questionnaire composed in accordance with the questionnaires used for examining attitudes towards foreign languages. The obtained results indicate that the students of both languages highly agree with the statement that the knowledge of foreign languages leads to a better social status, more successful socialisation and benefit in one’s professional career. In addition, they highly agree with the statement that the teaching materials are miscellaneous, but would nevertheless want the materials to include more elements of the domicile culture. Furthermore, the results also indicate a high level of confidence in speaking both languages and a substantial level of awareness regarding the need to speak both languages. The analysis confirmed a significant difference between the learners of Italian and German; the learners of German perceive the language as more difficult than Italian. However, such subjective evaluation does not have an adverse effect on the attitudes towards the German language.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.384
Teacher spread0.333 · 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 designObservational
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

Citations7
Published2017
Admission routes1
Has abstractyes

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