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Record W2586918455 · doi:10.1177/0047239516671945

Integrating Technology-Enhanced Collaborative Surfaces and Gamification for the Next Generation Classroom

2017· article· en· W2586918455 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

VenueJournal of Educational Technology Systems · 2017
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsOntario Tech UniversityYork University
Fundersnot available
KeywordsComputer scienceBlackboard (design pattern)MultimediaHuman–computer interactionProcess (computing)Mobile devicePerspective (graphical)Augmented realityCollaborative learningTable (database)Knowledge managementWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

We place collaborative student engagement in a nontraditional perspective by considering a novel, more interactive educational environment and explaining how to employ it to enhance student learning. To this end, we explore modern technological classroom enhancements as well as novel pedagogical techniques which facilitate collaborative learning. In our setup, the traditional blackboard or table is replaced by a digitally enabled interactive surface such as a smart board or a tabletop computer. The information displayed on the digital surface can be further enhanced with augmented reality views through mobile apps on student smartphones. We also discuss ways to enhance the instructional process through elements of game mechanics and outline an experimental implementation. Finally, we discuss an application of the proposed technological and pedagogical methods to human anatomy training.

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: none
Teacher disagreement score0.888
Threshold uncertainty score0.854

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.050
GPT teacher head0.331
Teacher spread0.281 · 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