MétaCan
Menu
Back to cohort
Record W3180690126 · doi:10.1080/02680513.2021.1936476

European Union Digital Education quality standard framework and companion evaluation toolkit

2021· article· en· W3180690126 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen Learning The Journal of Open Distance and e-Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsAlgonquin College
Fundersnot available
KeywordsEuropean unionProcess (computing)Computer scienceQuality (philosophy)Quality assuranceDistance educationEngineering managementProcess managementEngineeringMathematics educationBusiness

Abstract

fetched live from OpenAlex

The Covid-19 pandemic positioned digital education in a new light. The need for educational institutions to develop strategies, standards and establish quality assurance across digital education became even more evident. This paper describes the four-step process of designing an interactive European Union (EU) Digital Education Quality Standard Framework and Companion Evaluation Toolkit to guide the design, delivery and evaluation of effective digital education. (1) A review of literature of existing digital education frameworks and models is presented. (2) Variables and sub-variables inherent in designing, delivering and evaluating effective digital education are identified. (3) Next the variables and sub-variables in the framework are defined. (4) The process of designing the interactive framework diagram is described with the companion evaluation toolkit outlined. The proposed framework is flexible and applicable to entities and audiences regardless of where they are in the online learning adoption process.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0080.006
Open science0.0010.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.362
Teacher spread0.329 · 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