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Record W1253825657 · doi:10.4018/ijmbl.2015100104

A Design Based Research Framework for Implementing a Transnational Mobile and Blended Learning Solution

2015· article· en· W1253825657 on OpenAlex
Agnieszka Palalas, Nicole Berezin, Charlotte Nirmalani Gunawardena, Gretchen Kramer

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Mobile and Blended Learning · 2015
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsAthabasca University
Fundersnot available
KeywordsDesign-based researchSalientProcess (computing)Knowledge managementSociologyConceptual frameworkPedagogyComputer scienceEngineering managementEngineeringSocial science

Abstract

fetched live from OpenAlex

The article proposes a modified Design-Based Research (DBR) framework which accommodates the various socio-cultural factors that emerged in the longitudinal PA-HELP research study at Central University College (CUC) in Ghana, Africa. A transnational team of stakeholders from Ghana, Canada, and the USA collaborated on the development, implementation, and subsequent modification of the DBR framework. The recommended framework is a result of lessons learned during this project in Ghana and as such, it is shaped by the need to be responsive to the local cultural and contextual contingencies. The article offers practical recommendations on the implementation of a mobile learning project in a cross-cultural setting, and provides a discussion of the salient cultural factors and the corresponding culturally-sensitive adaptations needed in the design research process. The Cross-Culture Design-Based Research (CC-DBR) framework is proposed to inform future transcultural m-learning studies.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.094
GPT teacher head0.385
Teacher spread0.291 · 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