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Record W3008988913 · doi:10.5539/ies.v13n4p27

Using Educational Robotics as a Cognitive Tool for ICT Teachers in an Authentic Learning Environment

2020· article· en· W3008988913 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 · 2020
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyRoboticsScope (computer science)PsychologyCognitionMathematics educationEducational roboticsQualitative researchEducational technologyArtificial intelligenceLearning environmentComputer sciencePedagogyRobotSociologyWorld Wide Web

Abstract

fetched live from OpenAlex

This study describes the theoretical foundations of a learning environment designed for an ICT teachers’ graduate level course, and presents a comprehensive analysis of the qualitative data acquired regarding the course’s implementation. Participants in the study included six ICT teachers enrolled in the “Embedded Systems and Robotic Applications” course of Spring 2018. A design-based research approach was used in order to achieve a systematic but flexible methodology. Within the scope of this study, robotics was used as a cognitive tool, and authentic learning principles were applied. The participants’ reflections about the learning environment indicated that they were satisfied with the course settings, their motivation increased after the course, and they learned more effectively through the scaffolding provided by the instructor.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.121
GPT teacher head0.416
Teacher spread0.295 · 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