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Record W2888419048 · doi:10.5539/hes.v8n3p104

Corpus-based Evaluation on Instrumental Texts in Textbook

2018· article· en· W2888419048 on OpenAlexvenueno aff
Mengqi Zhang, Wenzhong Zhu, Wan Muchun

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)CognitionMathematics educationSalientTeaching methodContent analysisObject (grammar)PsychologyComputer sciencePedagogyArtificial intelligenceSociologySocial science

Abstract

fetched live from OpenAlex

Instrumental texts, as an important part of the textbooks, play an important role in organizing the teaching content and the teaching activities, facilitating the communication of the teacher, students and textbooks. Therefore, it is of great theoretical and practical significance to study and evaluate the instrumental texts of the textbooks. The instrumental texts in Market Leader is adopted as the research object. Content analysis and corpus analysis are applied in this paper. By retrieving their salient lexical and semantic patterns which are further associated with some modern concepts in ESP education, the book is evaluated from the perspective of cognitive strategy, learning styles, the relationship between teaching materials and learners and contextualized language instructions. The results of this study are as following: 1) Teaching notions can be reflected from the instrumental texts. 2) Cooperative learning and learners-centered approach are well shown in this book. Cognitive thinking and situation creation are relatively weak in this book.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0640.002

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 head0.076
GPT teacher head0.464
Teacher spread0.389 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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