Analyzing Factors Influencing Vocational High School IT Program Students' University Choices Using Association Rule Mining

Year 2024, Volume: 7 Issue: 2, 135 - 142, 31.12.2024

Esma Türk , Erkan Özhan

https://doi.org/10.55581/ejeas.1606948

Abstract

The complex masses of data that have emerged with increasing data generation and storage have increased the need for computers and software with more advanced computing capabilities to process this data. However, extracting meaningful information from complex data remains a challenge. Data mining, particularly in collaboration with artificial intelligence algorithms, works to uncover intricate relationships within data. One of the complex problems to be solved is guiding high school students toward university departments that will optimize their performance. This study investigates the factors influencing the university department preferences of vocational high school information technology students and graduates in the field of computer science. Unlike previous research, has typically focused on academic performance and current educational contexts, this study explores the connections among students' past educational experiences, preferences, habits, and hobbies, tracing these back to primary and secondary education. As a case study, the research centers on the computer engineering department, revealing that students who wish to study or are studying computer engineering show a greater interest in activities related to design and game development, have a preference for the C# programming language, and exhibit a particular interest in chemistry, while demonstrating less affinity for street games. These findings underscore the relationship between students' higher education preferences in computer science and their prior learning experiences and social preferences, offering deeper insights into the decision-making process.

Keywords

Artificial intelligence, Association rules, Career, Data mining, University department choice

Thanks

This study is part of the Master of Science thesis by Esma Türk, conducted in the Department of Computer Engineering within the Institute of Natural and Applied Sciences at Tekirdağ Namık Kemal University, under the supervision of thesis advisor Erkan Özhan. The authors would like to thank the Institute for its support and all survey participants for their valuable contributions.

Meslek Lisesi Bilişim Programında Üniversite Bölüm Tercihini Etkileyen Faktörlerin Birliktelik Kuralı ile Analizi

Abstract

Artan veri üretimi ve depolamasıyla birlikte ortaya çıkan karmaşık veri yığınları, bu verilerin işlenmesi için daha gelişmiş hesaplama yeteneklerine sahip bilgisayarlar ve yazılımlara olan ihtiyacı artırdı. Ancak, karmaşık verilerden anlamlı bilgiler çıkarmak hala bir zorluktur. Veri madenciliği bilimi, özellikle yapay zeka algoritmaları ile iş birliği içinde, verilerdeki karmaşık ilişkileri ortaya çıkarmak için çalışmaktadır. Çözülmesi gereken karmaşık sorunlardan biri de lise öğrencilerinin üniversitede en yüksek verimi sağlayacak bölüme yönlendirilmesidir. Bu çalışma, meslek lisesi bilişim teknolojileri öğrencilerinin ve mezunlarının bilgisayar bilimleri alanındaki üniversite bölüm tercihlerini etkileyen faktörleri araştırmaktadır. Genellikle akademik performans ve mevcut eğitim bağlamlarına odaklanan önceki araştırmalardan farklı olarak bu çalışma, öğrencilerin geçmiş eğitim deneyimleri, tercihleri, alışkanlıkları ve hobileri arasındaki bağlantıları ilk ve orta öğretime kadar izleyerek araştırmaktadır. Bir vaka çalışması olarak bilgisayar mühendisliği bölümüne odaklanan araştırma, bilgisayar mühendisliği okumak isteyen veya okumakta olan öğrencilerin tasarım ve oyun geliştirmeyle ilgili faaliyetlere daha fazla ilgi gösterdiğini, C# programlama dilini tercih ettiğini ve kimyaya özel bir ilgi gösterirken sokak oyunlarına daha az yakınlık gösterdiğini ortaya koymaktadır. Bu bulgular, öğrencilerin bilgisayar bilimleri alanındaki yüksek öğrenim tercihleri ile önceki öğrenme deneyimleri ve sosyal tercihleri arasındaki ilişkinin altını çizmekte ve karar verme sürecine dair daha derin bilgiler sunmaktadır.

Keywords

Birliktelik kuralları, Kariyer, Üniversite bölüm tercihi, Veri madenciliği, Yapay zeka

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