Selection of Outstanding Course Participants for Award Recipients Using the Topsis Method of Case Studies of Multilogic Course Institutions
Bibliographic record
Abstract
A course institution is an organization or institution that provides educational or training programs in various fields. This institution aims to improve the skills, knowledge, and competencies of course participants. Course institutions can operate formally or informally and offer different types of courses, ranging from short-term courses to more intensive programs. Examples of course institutions include vocational training centers, language schools, computer centers, and other professional educational institutions. In this study, the author wants to explain the determination of outstanding course participants by applying the TOPSIS method to get alternative students who are close to positive ideals and far from negative ideals, based on data on participant values, among others, course certificate assessment criteria, course average scores, skills, final project scores and attendance, the value of giving awards to participants in order to further improve the quality of participant achievement so that the participants are more enthusiasm in learning. The purpose of selecting outstanding students to give awards is to appreciate and recognize the efforts, abilities, and outstanding achievements shown by students. This award aims to motivate students to continue to excel, increase their enthusiasm for learning, and encourage healthy competition among participants. In addition, this award also serves as an inspiration for other students to strive to achieve the best results in their field of pursuit. After doing the preference value of each alternative, the largest score is owned by alternative V14 (Dwi Intan Sari) with a value of 0.801. It can be concluded that the recipient of the award for the outstanding participant in Multi Logika is Dwi Intan Sari.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".