The System Architecture of Intelligent Student Relationship Management Based on Cognitive Technology with Conversational Agent for Enhancing Student’s Loyalty in Higher Education
Bibliographic record
Abstract
This paper presents the conceptual framework, value chain model and the system architecture of intelligent student relationship management based on cognitive technology with conversational agent for enhancing student’s loyalty in higher education. The purposes were to synthesize the conceptual framework and apply it to develop the value chain model and the system architecture of intelligent student relationship management based on cognitive technology with conversational agent for enhancing student’s loyalty in higher education and assess the developed value chain model and system architecture. The questionnaire was employed as the instrument to assess and certify the value chain model and the system architecture by the experts. The 5 point-Likert scale was used to identify the level of agreement of the value chain model and system architecture certification assessment. The instrument was verified by five experts using content validity index (CVI). After that, the value chain model and the system architecture were verified based on the consensus assessments of seventeen experts using mean, standard deviation (S.D.), median, interquartile range and quartile deviation. The results revealed that the experts had a consensus on the value chain model developed based on the conceptual framework (Mean = 4.89, S.D. = 0.27, Median = 5, Interquartile Range: I.R. = 0.00, Quartile Deviation: Q.D. = 0.00). They also had a consensus to approve the system architecture developed based on the value chain model (Mean = 4.70, S.D. = 0.55, Median = 5, Interquartile Range: I.R. = 1.00, Quartile Deviation: Q.D. = 0.50).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".