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Record W1753006307 · doi:10.19173/irrodl.v16i5.2027

Examining Value Change in MOOCs in the Scope of Connectivism and Open Educational Resources Movement

2015· article· en· W1753006307 on OpenAlexvenueno aff
Hayriye Tuğba Öztürk

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2015
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsConnectivismOpen educational resourcesScope (computer science)AutonomyOpenness to experienceMassive open online courseValue (mathematics)PedagogyKnowledge managementSociologyPsychologyComputer sciencePolitical scienceLearning theory

Abstract

fetched live from OpenAlex

Massive Open Online Courses (MOOCs) came to prominence with Open Educational Resources Movement (OERM). It was based upon the idea of libre in removal of some permission barriers and gratis in removing the price barrier (Suber, 2008) in learning resources. In line with the theoretical underpinnings of OERM, MOOCs embody primary characteristics of connectivist pedagogy which are autonomy, diversity, openness, and community participation. However, in time, moving away from its original philosophical and pedagogical values, new variations of MOOCs have emerged and new MOOCs have become more market oriented and are aligned with instructivist, cognitive, and behaviourist pedagogy. In an attempt to empirically examine the change in underlying values of the MOOCs, a survey method was employed by using a Connectivist Learning Environment Assessment Tool which was developed in the scope of this research. The tool could be useful for formulating and justifying a conceptual framework that enables us to reify the connectivist pedagogy and assess connectivist underpinnings of a learning environment including MOOCs. This research aims to contribute to MOOC studies against the background of previous knowledge from the Open Education and Connectivist fields.

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)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.253
GPT teacher head0.486
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2015
Admission routes1
Has abstractyes

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