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Record W2591620141 · doi:10.21226/t2kg6q

Technologically Enhanced Language Learning and Instruction: Подорожі.UA: Beginners’ Ukrainian

2017· article· en· W2591620141 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2017
Typearticle
Languageen
FieldComputer Science
TopicInnovative Educational Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUkrainianBlended learningComponent (thermodynamics)Class (philosophy)Computer scienceMathematics educationFace (sociological concept)Experiential learningForeign languageLanguage acquisitionEveryday lifePedagogyEducational technologyPsychologySociologyLinguisticsArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

This article reports on the development of a new blended-learning model for beginners’ Ukrainian language learning and instruction, an innovative approach in foreign language education. This model is a combination of face-to-face and online learning and is a response to new realities in education, and language learning in particular, in our fast-paced, technologically enhanced everyday life. The authors focuses on the design of their new blended-learning textbook Подорожі.UA (Travels.UA), which contains a considerable online component, closely interconnected with in-class, or face-to-face, learning and teaching materials. They discuss their approach to the pedagogical design of this new model, used in the textbook, and also address piloting challenges. The study concludes with a report on the overall success of this project and invites others who teach Ukrainian at postsecondary levels to pilot the project in their institutions.

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.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.347
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