Covid-19 Pandemisi Sürecinde Öğrencilerin Akademik Öz Yeterlilik, Topluluk Hissi ve Akademik Başarılarının Veri Madenciliği Teknikleri ile Değerlendirilmesi

Öz

Günlük hayatın bir parçası haline gelen teknolojiler sayesinde hemen her alanda devasa veri yığınları oluşmaktadır. Büyük verideki gizli örüntülerin tespit edilmesi ve faydalı bilgilerin keşfedilmesine yönelik araştırmalar önem kazanmıştır. Eğitim alanında biriken veri miktarı, bu alanda geleneksel istatistiksel yöntemlere alternatif olarak veri madenciliği tekniklerinin ön plana çıkmasını sağlamıştır. Geleneksel istatistiksel yöntemlerde bazı değişkenler arasındaki gizli ilişkiler göz ardı edilebilmektedir. Bu da bazı bilgilerin kaybolmasına ya da eğitim gibi temel alanlarda gerekli verilerin kullanılamamasına neden olabiliyor. Ancak eğitimsel veri madenciliği (EVM), eğitimin kalitesini iyileştirmek ve geliştirmek için değerli verilerin kilidini açabilir ve önemli ilişkileri tahmin edebilir. Bu nedenle bu çalışma, EVM'nin tahmin gücüne dikkat çekmek için örnek bir EVM uygulaması gerçekleştirmeyi amaçlamıştır. Veri seti üniversite öğrencilerinden toplanan görüşlerden oluşmaktadır. Bu veri setinin değişkenlerini uzaktan eğitim öğrencilerinin akademik öz yeterlilikleri, topluluk hissi, akademik başarı ortalamaları ve bazı demografik değişkenler oluşturmuştur. Betimsel model, çalışmadaki değişkenler arasındaki örtük örüntüleri ortaya çıkarmış ve değişkenleri tahmin etmek için yordayıcı bir model kullanılmıştır. Bunun için birliktelik kuralı yöntemi ve sınıflandırma algoritması da kullanılmıştır. Çalışma sonunda EVM'nin değişkenler arasındaki ilişkileri etkili bir şekilde bulabildiği ve değişkenleri tahmin edebildiği sonucuna varılmıştır.

Anahtar Kelimeler

• eğitsel veri madenciliği
• akademik öz-yeterlilik
• topluluk hissi
• akademik başarı

Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques

Öz

Thanks to the advancement of technology, vast amounts of data are being generated in various fields on a daily basis. The research on identifying hidden patterns and extracting useful information from big data has become increasingly important. In the field of education, the availability of large datasets has allowed for the emergence of data mining techniques as an alternative to traditional statistical methods. Unlike traditional statistical methods, data mining can uncover hidden relationships between variables, thus avoiding the loss of valuable information and enabling the utilization of essential data in education. By unlocking valuable insights and predicting important relationships, educational data mining (EDM) has the potential to enhance and improve the quality of education. This study aims to demonstrate the predictive power of EDM through a sample application and draw attention to its implications. The dataset used in this study consists of survey responses collected from university students. The variables in the dataset include academic self-efficacy, sense of community, academic achievement averages, and various demographic variables of distance education students. Descriptive modeling was employed to identify latent patterns between variables, while a predictive model was utilized to estimate variables. In order to achieve this, both association rule mining and classification algorithms were employed. The findings of this study indicate that EDM can effectively identify relationships between variables and make accurate predictions.

Anahtar Kelimeler

• educational data mining
• academic self-efficiency
• community feeling
• academik achievement

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