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Record W2990510639 · doi:10.17770/etr2019vol2.4044

USING ACTIVITY THEORY FOR MODELLING TRANSFORMATIVE DIGITAL LEARNING

2019· article· en· W2990510639 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

VenueEnvironment Technology Resources Proceedings of the International Scientific and Practical Conference · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTransformative learningActivity theoryInterdependenceGovernment (linguistics)UkrainianSociologyMathematics educationPedagogyKnowledge managementComputer sciencePolitical scienceEngineering ethicsPsychologyEngineeringSocial science

Abstract

fetched live from OpenAlex

In support of ongoing educational transformation in post-Soviet nations, this article positions activity theory (in the tradition of Engeström) as a framework for modelling changes towards innovative forms of collabo-rative, fully online digital learning. A strength of activi-ty theory is that it adopts a holistic socio-technical per-spective in which teachers, learners, technologies, peda-gogical values, roles/identities and rules/cultures are considered together as interdependent elements of col-lective activity. An illustrative example is offered to model a current and envisioned (target) activity system. In addition, a few considerations to guide research are offered. These include an emphasis on measuring the general readiness of students and teachers, and the need to explore gender divides. The goal is to help envision program transformations towards online learning at two partner universities as part of Ukrainian and Lat-vian, government-funded projects.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.080
GPT teacher head0.347
Teacher spread0.267 · 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