Çevrimiçi Matematik Öğrenmede Öz-Düzenlemeyi Etkileyen Bileşenler: Deprem Sürecinde Bir Öz-Düzenleme Perspektifi

Abstract

Bu araştırmanın amacı, çevrimiçi matematik öğrenme sürecinde öz-düzenlemeli öğrenmeyi etkileyen değişkenleri belirlemek ve bu değişkenler arasındaki ilişkiyi incelemektir. İlişkisel tarama modelinde tasarlanan bu çalışma, Türkiye'deki bir devlet üniversitesinde çevrimiçi ders alan toplam 233 matematik öğretmeni adayı üzerinde yürütülmüştür. Öğretmen adayları kişisel bilgi formu, öz-düzenlemeli çevrimiçi öğrenme ölçeği ve çevrimiçi öğrenme öz-yeterlik ölçeğini içeren veri toplama araçlarını çevrimiçi olarak doldurmuştur. Veriler rastgele orman ve sınıflama ve regresyon ağacı yöntemleri kullanılarak analiz edilmiştir. Rastgele orman yöntemiyle oluşturulan modelin doğru sınıflama performansı çok yüksek düzeyde bulunmuştur. Öğretmen adaylarının öz-düzenlemeli çevrimiçi öğrenmelerini yordamada etkili olan değişkenlerden önem derecesi en yüksek olanlar öz-yeterlik, öz-değerlendirme ve sınıf düzeyi değişkenleridir. Diğer önemli değişkenler ise ders tercihi, internet kullanma düzeyi ve internet kullanma sıklığıdır. Elde edilen sonuçlar doğrultusunda bu çalışmanın öğretmen adaylarının öz-düzenlemeli çevrimiçi öğrenmelerinin geliştirilmesi noktasında öngörüde bulunmaya, çevrimiçi öz-düzenlemeli öğrenme süreçlerine ve öz-yeterlik algılarının gelişmesine rehberlik etmeye önemli katkısının olacağı düşünülmektedir.

Keywords

• Öz-düzenleme
• Öz-Yeterlik
• Öz-Değerlendirme
• Rastgele Orman Yöntemi
• Sınıflandırma ve Regresyon Ağacı Yöntemi

Components Affecting Self-Regulation in Online Mathematics Learning: A Self-Regulation Perspective During the Earthquake

Abstract

The aim of this research is to determine the variables that affect self-regulated learning in the online mathematics learning process and to examine the relationship between these variables. The study, designed in the relational screening model, was conducted on a total of 233 prospective mathematics teachers taking online courses at a state university in Turkey. Teacher candidates filled out data collection tools online, including a personal information form, self-regulated online learning scale and online learning self-efficacy scale. Data were analyzed using random forest and classification and regression tree methods. The correct classification performance of the model created by the random forest method was found to be very high. Among the variables that are effective in predicting teacher candidates' self-regulation online learning, the ones with the highest importance are self-efficacy, self-evaluation and grade level variables. Other important variables were determined as course preference, level of internet use and frequency of internet use. In line with the results obtained, it is thought that this study will make a significant contribution to making predictions about the development of online self-regulation learning of teacher candidates, guiding their self-regulated and development of self-efficacy in the online learning process.

Keywords

• Self-Regulation
• Self-Efficacy
• Self-Evaluation
• Random Forest Method
• Classification and Regression Tree Method.

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