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Record W4416256156 · doi:10.71060/8p6p7c70

SHAPING ONLINE PEDAGOGY: FACULTY EXPERIENCES ON INSTRUCTIONAL DESIGN AND ODEL COURSE DEVELOPMENT IN A PRIVATE UNIVERSITY IN KENYA

2025· article· W4416256156 on OpenAlexaff
Rebecca Wambua

Bibliographic record

VenueJournal of Contemporary Issues in Open Distance and E-Learning · 2025
Typearticle
Language
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsInstructional designADDIE ModelDistance educationInstructional simulationOpen educational resourcesQuality (philosophy)Educational technologyQualitative propertyCourse evaluation

Abstract

fetched live from OpenAlex

Open Distance and e-Learning is a global phenomenon which has grown exponentially in institutions of higher learning. Among the strategic areas in provision of quality services in ODeL is the provision of instructional materials. Instructional materials statistically and significantly, positively influence the academic performance of distance learning students. University of Eastern Africa, Baraton, embarked on the journey to create instructional materials which would enhance the implementation of online learning, through a 2 (two) day workshop on instructional design and ODeL course development. The specific objectives were to describe ADDIE Model and Co-Creation Model, evaluate E-Content Design and Development, including overview of Open Educational Resources (OER), analyze the structure of a blended learning Course material, describe Instructional Material Development including use of OER and develop Instructional Materials including integrating OER. A mixed method study design was used to explore strategies for effective design and development of instructional materials including OER at University of Eastern Africa, Baraton, Kenya. A campus-wide survey assessed knowledge and experience in instructional design for ODeL Course materials, ADDIE model, Co-Creation Model, integration of OER into course, OER repositories, and familiarity with virtual learning platforms, knowledge and experience on Quality Assurance of OER and whether the participants had technical skills in terms of creating ODL content. One hundred (100) staff were purposively engaged for the study through discussions on design and development of instructional materials with pre- and post-workshop surveys capturing individual perspectives. Quantitative data underwent statistical analysis, while qualitative data was thematically analyzed. The study recommended the need for faculty in universities to be trained in e-content design and development for enhancing ODeL program quality,

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.002
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 head0.067
GPT teacher head0.364
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.

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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