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Record W2893096203 · doi:10.19173/irrodl.v19i4.3394

A Framework for Implementing OER-Based Lesson Design Activities for Pre-Service Teachers

2018· article· en· W2893096203 on OpenAlexvenueno aff
Dongho Kim

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesComputer scienceInstructional designProcess (computing)Service (business)Knowledge managementResource (disambiguation)Teacher educationProfessional developmentEducational technologyFaculty developmentOpen educationMathematics educationMultimediaPedagogyPsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

The demand for qualified teachers with sufficient pedagogical knowledge and skills is high. However, existing teacher education programs do not provide adequate experiences through which to develop pre-service teachers’ professional foundations. This study recognized Open Educational Resources (OER) as a means by which to address the issue of enhancing teacher education. The purpose of this study was to propose a framework to be used to integrate OER into lesson design activities for pre-service teachers. In this study, a focused literature review investigated the frameworks of distributed cognition and example-based learning. This review process resulted in a unified framework that provides a description of how pre-service teachers learn with OER at both the individual and cognitive system levels. Four principles and 10 guidelines are provided to guide the implementation of OER-based lesson design activities in real settings. The new framework has the potential to enhance pre-service teachers’ Web resource-based professional development.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.207
GPT teacher head0.508
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations17
Published2018
Admission routes1
Has abstractyes

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