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Record W3153139467 · doi:10.5539/hes.v11n2p147

The Student Relationship Management System Process with Intelligent Conversational Agent Platform

2021· article· en· W3153139467 on OpenAlexvenueno aff
Siriluk Phuengrod, Panita Wannapiroon, Prachyanun Nilsook

Bibliographic record

VenueHigher Education Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersKing Mongkut's University of Technology North Bangkok
KeywordsProcess (computing)Dialog systemIntelligent agentComputer scienceField (mathematics)Process managementManagement systemKnowledge managementMulti-agent systemIntelligent decision support systemHuman–computer interactionWorld Wide WebArtificial intelligenceEngineeringOperations management

Abstract

fetched live from OpenAlex

The objectives of the study were as follows: (1) to study the student relationship management system process with intelligent conversational agent platform, (2) to design the student relationship management system process with intelligent conversational agent platform, and (3) to evaluate the student relationship management system process with intelligent conversational agent platform provided by seven experts who had experience in a related field. The study findings suggested that the student relationship management system process with intelligent conversational agent platform consists of four main dimensions: (1) Strategic, (2) Operational, (3) Analytical, and (4) Collaborative. After analyzing the data, it showed that the overall result of the evaluation of the student relationship management system process with intelligent conversational agent platform was at a very high appropriate level, which can be applied to real situations.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.081
GPT teacher head0.341
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2021
Admission routes1
Has abstractyes

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