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Teaching Computer Science with CLIL Methodology

2020· article· en· W3119351700 on OpenAlexaboutno aff
Olena Hrytsiuk

Bibliographic record

VenueEngineering and Educational Technologies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsForeign languageCurriculumComputer scienceLesson planSubject (documents)GlossaryGrammarPlan (archaeology)Content and language integrated learningMathematics educationTeaching methodLanguage educationPedagogySociologyLinguisticsLibrary sciencePsychology

Abstract

fetched live from OpenAlex

In this paper, methodological and organizational aspects of applying CLIL methodology in teaching computer science for students of universities are analyzed. It is shown that European countries, as well as the United Kingdom, the United States of America, and Canada, where part of the curriculum has been taught in a foreign language for several decades, are most actively introducing content and language integration and promoting an interdisciplinary approach to education. CLIL methodology features and benefits are identified, namely, a comprehensive focus, stimulating learning environment, authenticity, active learning, gradual learning and collaboration. The peculiarity of CLIL methodology is that its use requires a stable elementary skill in the grammar of a foreign language, as well as knowledge of subject-specific language. In accordance with various conditions, CLIL model can be presented in various forms: it can be a full course of a foreign language discipline, a module from a specific topic area, a part of any course, a project, a laboratory workshop, and a research, too. The paper presents a plan for computer science lectures and labs in a foreign language, which contains seven stages, each of which is performed in English. Integration of computer science and teaching in English at the Kremenchuk Mykhailo Ostrohradskyi National University is implemented using developed bilingual course program “Computer for beginners”. The course content includes a study of the material necessary for obtaining skills in using modern ICT and a personal computer using English glossary. Conclusions are drawn regarding conditions and principles of teaching computer science in a foreign language. The scientific novelty of the work is that for the first time a comprehensive analysis of the expediency of using CLIL methodology was carried out at the Kremenchuk Mykhailo Ostrohradskyi National University. The practical significance of the work is that its results can be used for further implementation of content and language integration in Ukraine universities. The development of new content and language integrated courses with CLIL methodology for teaching courses in a foreign language is considered promising.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.004

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 head0.045
GPT teacher head0.257
Teacher spread0.211 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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