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Record W4405187362 · doi:10.47392/irjaeh.2024.0373

EDUSYNC – Smart Classroom Management System

2024· article· en· W4405187362 on OpenAlexaff
Mr. Jaganath, Nandhana, Dharshini, Monesha, Dhanya

Bibliographic record

VenueInternational Research Journal on Advanced Engineering Hub (IRJAEH) · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsComputer scienceBusiness

Abstract

fetched live from OpenAlex

A Smart Classroom Management System (SCMS) named EDUSYNC was developed to enhance learning and simplify classroom operations. This system incorporates elements of Learning Management Systems (LMS) and Content Management Systems (CMS) by utilizing modern technologies which include React.js, Node.js, MongoDB, and Large Language Models. It promotes active learning by giving students interactive tests in programming languages like Python, C++, and Java. By enabling teachers to oversee grades, authorize duty requests, and monitor student absences, the platform promotes mentor-mentee engagement. Students can use a built-in resume generator to produce professional resumes and use bar and pie charts to visualize their academic achievement. Parents can also keep track of their children's attendance and leave records and keep an eye on their academic progress. Chatbots are incorporated into EDUSYNC to help students with their educational queries improving

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0620.022

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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