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Record W2888427178 · doi:10.3968/10210

Teaching Mode Exploration Basing on the Text of New Horizon College English Text Book for Reading and Writing

2017· article· en· W2888427178 on OpenAlexvenueno aff
Chang Zheng

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicForeign Language Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyActive listeningReading (process)Class (philosophy)MemorizationComputer sciencePoint (geometry)Mathematics educationCollege EnglishLinguisticsPsychologyArtificial intelligenceCommunicationMathematics

Abstract

fetched live from OpenAlex

The New Horizon College English Text Book for Reading and Writing embodies the application-oriented designing concept that combines listening, speaking, reading and writing. It puts students’ comprehensive application ability at the forefront. The core is to develop students’ ability to comprehensively applying language. Teachers must use this as the basis for selecting teaching mode in the process of using teaching materials. This paper takes New Horizon College English Text Book for Reading and Writing as an example, combining with practical experience, to discuss how to teach the text, and to propose the teaching mode, which contains vocabulary, discourse and linguistic points, and cognition strategy. For vocabulary teaching, the ability, which truly enables learners to understand and use words accurately, needs to emphasize learning vocabulary in communication through systematic principle, communication principle, cultural principle, cognitive principle, and emotional principle. Discourse teaching is the main body of the text teaching, main mean and approach to develop students’ ability of rapid reading and understanding. It requires teaching the text as an organic integrity, and first, it must be understood and explained. For linguistic point teaching, students are more opposed to simply making a list of language usages and then exemplifying them. In fact, students are more in favor of “connecting, discovering rules, understanding and memorizing, exercising key points, and using reverse thinking to standardize the language.” Overall, text teaching design is the main body of in-class teaching. And in-class activities should focus on this subject, as well. The in-class teaching activities should be a mind-innovation process including learning, thinking, deliberating, inquiring, reasoning and judging. Therefore, teachers should focus on these aspects, continuously deepening the text book. In addition, the teachers’ thought will be explored during the process.

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.055
GPT teacher head0.407
Teacher spread0.353 · 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
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

Citations0
Published2017
Admission routes1
Has abstractyes

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