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Record W2518707183 · doi:10.58459/icce.2013.1042

A Conceptual Framework of the Use of Mobile Augmented Reality in Peer Assessment

2013· article· en· W2518707183 on OpenAlexaff
K. Kinshuk, Kuo-En Chang, Yao Ting Sung, Stefan Chao, Kai H. Chang

Bibliographic record

VenueInternational Conference on Computers in Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAugmented realityConceptual frameworkComputer scienceHuman–computer interactionPsychologyProcess managementEngineeringSociology

Abstract

fetched live from OpenAlex

This study presents a conceptual framework of applying mobile augmented reality technology on peer assessment to reduce the bounds of work reviews and assessment. According to mobile technology and augmented reality, students can show their design in various ways at di fferent places or in different situations. This paper proposes a novel mobile peer-assessment system which combines augmented reality with the reviewing and assessing processes. This framework enable s students to enhance work interpretation, frequently interact with peers, represent their thinking and reflect upon their own works. Furthermore, the mobile AR technique provides personalized and location-based adaptive contents that enable individual students to interact with the mixed reality environment and to observe how works are possibly applied to the real world in the future. The whole process assists students in reviewing works based on various dimensions, gaining proper knowledge, cultivating critical thinking skills and reflection as well as promoting meaningful learning.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.454
Teacher spread0.333 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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