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Record W3163142592 · doi:10.19173/irrodl.v22i2.5187

Design Matters: Development and Validation of the Online Course Design Elements (OCDE) Instrument

2021· article· en· W3163142592 on OpenAlexvenueno aff
Florence Martin, Doris U. Bolliger, Claudia Flowers

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersOrganisation de Coopération et de Développement Économiques
KeywordsLikert scalePsychologyInstructional designOnline coursePresentation (obstetrics)Computer-assisted web interviewingOnline learningScale (ratio)Distance educationMathematics educationMultimediaComputer scienceMathematicsCartographyMedicineGeographyStatisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

Course design is critical to online student engagement and retention. This study focused on the development and validation of an online course design elements (OCDE) instrument with 38 Likert-type scale items in five subscales: (a) overview, (b) content presentation, (c) interaction and communication, (d) assessment and evaluation, and (e) learner support. The validation process included implementation with 222 online instructors and instructional designers in higher education. Three models were evaluated which included a one-factor model, five-factor model, and higher-order model. The five-factor and higher-order models aligned with the development of the OCDE. The frequency of use of OCDE items was rated above the mean 4.0 except for two items on collaboration and self-assessment. The overall OCDE score was related to self-reported levels of expertise but not with years of experience. The findings have implications for the use of this instrument with online instructors and instructional designers in the design of online courses.

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)0.008
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 integrity0.0000.000
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 head0.167
GPT teacher head0.468
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 designOther design
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

Citations40
Published2021
Admission routes1
Has abstractyes

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