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Record W2982429744 · doi:10.5430/ijhe.v8n7p62

Modeling Dialogues in FL Class

2019· article· en· W2982429744 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicInnovations in Education and Learning Technologies
Canadian institutionsnot available
FundersKazan Federal University
KeywordsClass (philosophy)ConversationContinuationMathematics educationBehavioral patternPsychologyComputer scienceTeaching methodArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

The article gives a brief review on how to improve students’ speaking skills via modeling dialogues in FL class. This study can be considered the continuation of the research undertaken by the authors in2018, inwhich the authors put forward the hypothesis that students’ speaking skills improve provided their ability to match speech patterns to behavioral patterns is developed. An aggressive behavioral pattern was researched. In this study the authors evolve that idea and analyze two more behavioral patterns: friendly and neutral. The researchers claim that the better students know different behavioral patterns, the more effectively they model dialogues. Watching videos and commenting on the conversation strategies, and modeling dialogues are chosen to be the leading teaching methods approbated in the multi-stage experiment to favor the researchers’ idea. The obtained results indicate the high potential of the chosen teaching methods for the improvement of students’ speaking skills in FL class and can substantially help FL teachers to adopt the most effective teaching styles, based on their course learning objectives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.325
Teacher spread0.302 · 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