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Record W4385723118 · doi:10.23977/aetp.2023.070814

Blended Learning Based on BOPPPS—The Case of Oral English

2023· article· en· W4385723118 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

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Educational Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningCollege EnglishComputer scienceTest (biology)Mathematics educationCitizen journalismBridge (graph theory)PsychologyEducational technologyMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

In order to improve the traditional oral English teaching in college English in China, the study adopts blended learning based on BOPPPS teaching model, which reconstructs the traditional Oral English teaching. Based on BOPPPS, the study divides the teaching procedure into Bridge-in, Objectives (Outcomes), Pre-assessment (Pre-test), Participatory Learning, Post-assessment(Post-test) and Summary. The use of blended learning tools, which include Mini Program, Word Cloud and learning management system, effectively motivate learners. The teaching practices in Oral English from 2017 till 2022 show that the blended learning based on BOPPPS teaching model can effectively improve the learners' oral English, cultivating learners' cooperative and expressive abilities.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.442
Teacher spread0.414 · 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