Decision Support System on Faculty Profiling Using Full-Text Search Algorithm: A Tool for Evaluating Faculty Performances
Bibliographic record
Abstract
In any organization, management information system (MIS) is vital in increasing the efficiency and speed of decision-making processes.It improves the organization's control, competitiveness, and ability to make futuristic decisions.Faculty profile is the center of universities and colleges of which qualification and performances are vital in achieving and sustaining the mission and vision of the universities.Faculty profiling is a management information system that manages, monitors, and analyzes faculty records, assisting administrators in making suitable academic decisions.Generally, this research aims to develop a software entitled "Decision Support System on Faculty Profiling" using full-text search algorithm.This particularly manages faculty profiles, processes and analyzes records and data, and provides real-time feedback to both faculty and administrators, such as graphical and textual reports and recommendations.This keeps track faculty performance, skill set, field of specialization, experiences, and appointments which further assists management in making academic decisions, especially in terms of promotion and tenure.The development of the system was guided by the waterfall methodology model.Moreover, to ensure high-quality software, the researchers used a standardized instrument from International Organization for Standardization (ISO) 25010.Employing purposive sampling, 100 participants took part in the study which include 55 academic personnel, one human resource coordinator, one department chair, and ten software engineers.The system achieved an outstanding adjectival rating, indicating that it is operating in accordance with the set objectives, and meets all ISO 25010 requirements.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".