Student Relation Management on Cloud Technology for Academic and Internship Counseling Model in Rajabhat University
Bibliographic record
Abstract
The purposes of this study were: 1) to design a model of student relation management system on cloud technology for academic and internship counseling in Rajabhat University and 2) to assess a model of student relation management system on cloud technology for academic and internship counseling in Rajabhat University. The research methods were of 2 stages: 1) the model design stage in which 1.1) documents, textbooks and related literature were reviewed and analyzed, and 1.2) the model was designed, using elements and guidelines obtained from those documentaries together with the Front-end Analysis of needs, learners, environment and technology; and 2) the model assessment stage which included 5 steps, namely, 2.1) constructing instruments for model assessment, 2.2) having 9 experts to assess the instruments, 2.3) analyzing the data obtained from the assessment, using mathematic mean (x̄) and standard deviation (S.D.), 2.4) improving the model according to the experts ‘advises, and 2.5) presenting the model. It was found that the designed model was made up of 3 main components: 1) student relation management which consisted of 1.1) data base, 1.2) cloud technology, 1.3) creating relation, and 1.4) maintaining relation; 2) student relation activities of the management system on cloud technology that comprised 2.1) filtering teacher students’ demographic data, 2.2) connecting the data from the university with the university system, 2.3) publicizing information and news, 2.4) formulating academic counseling plan, 2.5) carrying out academic counseling, and 2.6) following up and evaluating; and 3) academic and internship counseling which was composed of 3.1) registration, 3.2) orientation, 3.3) supervision planning, 3.4) supervising, 3.5) academic counseling, following up the academic counseling, and 3.6) evaluation. The study revealed that the developed model was very appropriate to be used for academic and internship counseling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".