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Record W4407890868 · doi:10.5430/jct.v14n1p302

The Establishment of Task-Based Teaching Design Courses for Developing Pedagogical Design Ability of Pre-service Teachers in Chinese Normal Universities

2025· article· en· W4407890868 on OpenAlexvenueno aff
Xiaoyong Lin, Pengfei Chen, Zhenyu Huang

Bibliographic record

VenueJournal of Curriculum and Teaching · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Mathematics educationService (business)PsychologyPedagogyComputer scienceEngineeringSystems engineeringBusiness

Abstract

fetched live from OpenAlex

This study presents the design process of a task-based teaching design course in Chinese universities. The aim is to enhance the teaching design capabilities of candidates in Chinese language and literature while addressing the shortcomings of current teaching design courses in these institutions. To achieve this, task-based teaching has been incorporated into the curriculum. Utilizing the task-based teaching framework, the course was created through discussions among a group of experts. Once the course design was finalized, it was implemented for a semester at a university in China. The primary goal of this course is to foster the teaching design skills of Chinese language and literature pre-service teachers. This study outlines the course structure, designs teaching units, and details teaching activities among other components. Additionally, it explores the factors that should be considered when developing a task-based teaching design course. The findings aim to provide guidance for universities looking to implement such a course and to improve the teaching design capabilities of pre-service teachers in Chinese language and literature.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.026
GPT teacher head0.324
Teacher spread0.297 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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