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Record W6995908086

Pre-service Teachers’ Readiness for Online Learning

2017· article· en· W6995908086 on OpenAlexfundno aff

Bibliographic record

VenueVan Yüzüncü Yıl University Academic Data Management System · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
FundersUniversity of California, San FranciscoUniversity of Illinois at Urbana-ChampaignUniversitetet i OsloUniversity of California, Los AngelesUniversity of WaterlooUniversity of Nebraska-LincolnUniversity of MemphisUniversity of South AustraliaUniversity of Technology SydneyUniversity of Northern British ColumbiaUniversity of RoehamptonUniversity of GreenwichWestern Kentucky UniversityVirginia Commonwealth UniversityUniversity of Otago
KeywordsOnline learningOnline discussionFocus groupOnline assessmentFocus (optics)Online research methodsElectronic learningOnline courseData collection
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to determine pre-service teachers’ readiness foronline learning. In this study, existing situation has been descripted. Descriptive surveymethod was used. The participants of the study were pre-service teachers taking teachingcertification through online education in the fall semester 2015-2016. All of the participantstook part in the study voluntarily. Seven of the participants were female, two of them male.The data were collected through the focus group interviews including questions about thereadiness for online learning. The interview questions were formed in light of the literatureand examined by the expert in this field. The questions were organized under four categories,namely, “communication self-efficacy”, “institutional support”, “self-directed learning”, and“learning transfer self-efficacy”. Obtained data are grouped under the category of positive,negative and neutral. There are 68 positive, 7 neutral and 20 negative expressions. Whenproportioned, positive statements are approximately three and half times more than negativestatements. By this results we can say participants’ readiness for online learning is atsufficient level.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.003
Open science0.0030.001
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 head0.168
GPT teacher head0.322
Teacher spread0.154 · 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.

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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