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Record W2748826789 · doi:10.5539/ies.v10n9p87

Refining a Competency Model for Instructional Designers in the Context of Online Higher Education

2017· article· en· W2748826789 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
FundersCentral China Normal UniversityMinistry of Education, India
KeywordsContext (archaeology)Computer scienceKnowledge managementInstructional designHigher educationUnit (ring theory)Empirical researchMathematics educationPsychologyMultimedia

Abstract

fetched live from OpenAlex

This study investigates the instructional designers (IDs) competencies essential for the context of online higher education, and has selected an instruction design unit in a research university as a case of investigation. To identify and compare IDs competencies at organizational and individual levels, this study employed a mixed method to collect and analyze data based on a validated IDs competency model by the International Board of Standards for Training, Performance and Instruction (ibstpi) as a framework. Throughout the study, IDs’ expected jobs/tasks and currently performed jobs/tasks were systematically analyzed, and the applicability of the ibstpi model in this specific context of online higher education was verified. Based on the empirical findings, this study proposed a refined competency model to improve IDs performance in human resources development and management practice.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.270
GPT teacher head0.501
Teacher spread0.230 · 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