KEGIATAN WORKSHOP UNTUK MENINGKATKAN JUMLAH MAHASISWA PROGRAM STUDI TEKNIK
Bibliographic record
Abstract
Minat calon siswa pada program studi teknik di perguruan tinggi swasta dan negeri semakin berkurang. Data menunjukan hanya 14% mahasiswa yang memilih program studi teknik. Pada lingkup yang lebih kecil, jumlah peminat mahasiswa teknik di salah satu universitas swasta hanya 25% dibandingkan program studi ilmu sosial. Tujuan dari dilakukannya penelitian ini adalah untuk merumuskan penyebab rendahnya minat calon mahasiswa terhadap program studi teknik sekaligus mencoba untuk menyusun solusi-solusi aplikatif yang sudah dicoba, teori-teori yang relevan, yang dirasa dapat membantu dalam memecahkan masalah. Metode yang digunakan dalam penelitian ini adalah deskriptif kuantitatif. Data yang digunakan berasal dari hasil kuesioner mahasiswa baru dan data-data terkait dengan jumlah mahasiswa baik ditingkat kopertis III maupun di internal Universitas ABC. Berdasarkan hasil analisis data, ditemukan bahwa program studi teknik yang menjadi minat masyarakat kini adalah program studi teknik yang memiliki social senses, diantaranya Teknik Arsitektur, PWK dan Desain Produk. Sejalan dengan teori 4 (empat) unsur dibalik popularitas ketokohan seseorang, hal tersebut didasari akan trend calon mahasiswa terhadap daya tarik ilmu sosial yang bisa menemukan problem solving, kesuksesan public figure, dan industry branding. Upaya meningkatkan jumlah peminat program studi teknik, dapat dilakukan lewat kegiatan workshop dosen keilmuan kepada calon mahasiswa secara langsung serta open house universitas. Kata kunci: kuliah, minat, strategi, teknik Title: Workshop to Increase the Number of Students at Engineering Study Program Interest of prospective students toward engineering courses both in public and private universities keep decreasing. Data shows that only 14% of students choose engineering courses. In smaller scale, in one private university, students interest taking the engineering course are only 25% compared to student interest on taking the social courses. Purpose of this research was to conclude the reasons why student’s interest toward engineering courses are low, and to comprise some proven solutions, related theories, in order to help solving the problems. Method used in this research is descriptive quantitative. Data used comes from new student’s quisionaire, and related data with student’s number studying engineering course in Kopertis III and in the internal of ABC University. Data analysis showed that current engineering courses which have more students, are engineering courses with social senses such as Architecture, Regional Planning, and Design Product. Along with the theory of 4 (four) aspecs behind the popularity of a figure, all caused by the current trend of prospective student’s interest towards the social study attractiveness which leads to problem soving, the existence of success’s public figures, and industry branding. Direct workshop between lecturer and prospective students, and doing open house in the university could be strategies to increase the numbers of prospective student who want to take engineering course. Keywords: lecture, interest, strategy, technique
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.027 |
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".