Veri Madenciliği İle Türkiye’deki Ve Avrupa Birliği Ülkelerindeki Bilgisayar Mühendisliği Programlarının Karşılaştırılması
Bibliographic record
Abstract
Using data mining in many areas, it is tried to obtain useful information for people from meaningless data sets.In this study, data mining was used in the education field.This study was a comparison of computer engineering at universities in European Union countries and Turkey intended in the course of classified data mining methods with each other.Erasmus program agreements with 29 pieces of computer engineering university course listings in comparison to the 80 European Union countries with this university computer engineering course listings in Turkey, and these listings have been collected and analyzed.These data obtained were analyzed by Weka platform using Naive Bayes algorithm and J48 decision tree algorithm, which are among data mining classification algorithms.In order to examine the courses, the courses are divided into 9 classes as "Basic Courses, Mathematics Courses, Software Courses, Hardware
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".