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Record W3017099265 · doi:10.19173/irrodl.v20i5.4467

Investigating the Concerns of First-Time Distance Education Instructors

2019· article· en· W3017099265 on OpenAlexvenueno aff
Halil Kayaduman, Turgay Demirel

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationPsychological interventionAdaptation (eye)Medical educationPsychologyProcess (computing)PedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

The purpose of the study is to investigate the concern developments of first-time distance education instructors using the concerns-based adoption model (CBAM). This study used stages of concern (SoC), a component of CBAM, as its theoretical framework. A descriptive case study was implemented, which focused on the adaptation processes of nine instructors lecturing for the first time via distance education. The instructors attended a two-day training, which was designed based on their initial concerns. Then instructors implemented their courses for four weeks via distance education. While the informational and personal stages (self-concerns) decreased compared to the initial findings, the consequence stage increased in intensity. However, self-concerns remained predominant in the process despite the reduction in self-concerns and increase in the consequence stage. Based on the findings, the implications for distance education and recommendations for addressing the instructors’ concerns are discussed. Recommendations for alleviating the concerns of first-time distance education instructors include: the provision of ongoing concern-based interventions that incorporate technological, pedagogical, and content knowledge; providing working examples related to distance education from which instructors can learn vicariously; and encouraging collaboration among instructors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.521
Teacher spread0.391 · 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 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

Citations26
Published2019
Admission routes1
Has abstractyes

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