Kim Tamamlar, Kim Sertifika Alır? MOOC'larda Dijital İzler ve Öğrenme Çıktıları

Abstract

Bu çalışma, Kitlesel Açık Çevrimiçi Kurslar (MOOC'lar) bağlamında katılımcılar arasında kurs tamamlama ve sertifikasyonu etkileyen faktörleri araştırmayı amaçlamaktadır. Lojistik regresyon analizleri, katılımcıların demografik özelliklerine ve Massachusetts Teknoloji Enstitüsü (MIT) tarafından sunulan "Yapıların Öğeleri" adlı çevrimiçi kurstaki öğrenme etkileşimlerine dayanarak gerçekleştirilmiştir. Analizler, lisansüstü eğitim düzeyi, yaş grupları (özellikle 45 yaş ve üzeri), etkileşim günü sayısı, video oynatma sayısı, tamamlanan bölüm sayısı ve yazma etkinliği gibi değişkenlerin hem kurs tamamlama hem de sertifika alma olasılığı üzerinde anlamlı etkileri olduğunu ortaya koymuştur. Yazılı etkileşim ve içerik tamamlamanın başarıya katkısının özellikle anlamlı olduğu bulunmuştur. Model performansı, ROC eğrileri ve AUC (Eğri Altındaki Alan) kullanılarak değerlendirilmiştir. Sırasıyla 0,80 ve 0,92 olan AUC değerleri, yüksek sınıflandırma doğruluğu göstermiştir. Bulgular, lojistik regresyon analizinin MOOC platformlarında kullanıcı başarısını tahmin etmek için öngörücü modeller geliştirmede etkili bir araç olduğunu ve kişiselleştirilmiş, etkileşim odaklı öğrenme ortamları tasarımı için stratejik öneriler sunduğunu ortaya çıkarmaktadır.

Keywords

• Kitlesel Açık Çevrimiçi Dersler (MOOC
• Eğitim Veri Madenciliği
• Lojistik Regresyon
• Kurs Tamamlama
• Sertifikasyon

Who Completes, Who Gets a Certificate? Digital Traces and Learning Outcomes in MOOCs

Abstract

This study aims to investigate the factors affecting course completion and certification among participants in the context of Massive Open Online Courses (MOOCs). Logistic regression analyses were conducted based on participants' demographic characteristics and learning interactions in the online course "Elements of Structures" offered by the Massachusetts Institute of Technology (MIT). The analyses revealed that variables such as graduate education level, age groups (especially ages 45 and above), number of days of interaction, number of video plays, number of chapters completed, and writing activity had significant effects on the likelihood of both course completion and certification. The contribution of written interaction and content completion to success was found to be particularly significant. Model performance was evaluated using ROC curves and AUC (Area Under the Curve). The AUC values of 0.80 and 0.92, respectively, demonstrated high classification accuracy. The findings reveal that logistic regression analysis is an effective tool in developing predictive models for predicting user success on MOOC platforms and offer strategic recommendations for personalized, interaction-oriented design of learning environments.

Keywords

• Massive Open Online Courses (MOOCs)
• Educational Data Mining
• Logistic Regression
• Course Completion
• Certification

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