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Current Trends in Smart Classrooms and Sustainable Internet of Things

2024· book-chapter· en· W4402774265 on OpenAlex
Eriona Çela, Mathias Fonkam, Philip Eappen, Narasimha Rao Vajjhala

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

VenueAdvances in business information systems and analytics book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsCape Breton University
Fundersnot available
KeywordsInternet of ThingsInternet privacyComputer sciencePsychology

Abstract

fetched live from OpenAlex

This chapter explores the evolving landscape of smart classrooms, focusing on the integration of sustainable internet of things (IoT) technologies. This chapter systematically reviews current trends, highlighting how IoT innovations are reshaping educational environments to enhance learning experiences and improve resource efficiency. The chapter examines the impact of smart technologies on classroom design, teaching methodologies, and sustainability practices, offering insights into the benefits and challenges of adopting IoT in education. This chapter underscores the potential of sustainable IoT solutions to create more adaptive, efficient, and environmentally conscious educational spaces, setting the stage for the future of education.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.983
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.012
GPT teacher head0.291
Teacher spread0.279 · 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