Çevrimiçi Derslerde Dijital Okuryazarlık ve Teknostres Arasındaki İlişkinin İncelenmesi: İstatistiksel Analiz ve Random Forest Makine Öğrenmesi Tabanlı Öngörüsel Sağlamlık Değerlendirmesi

Abstract

Bu çalışma, çevrimiçi ders alan üniversite öğrencilerinde dijital okuryazarlık düzeyi ile teknostres düzeyi arasındaki ilişkiyi incelemeyi ve elde edilen bulguları Random Forest tabanlı makine öğrenmesi ile öngörüsel sağlamlık açısından değerlendirmeyi amaçlamaktadır. Araştırma, Ankara’daki bir vakıf üniversitesinin uzaktan eğitim programlarına kayıtlı 385 öğrenci üzerinde yürütülmüştür. Katılımcıların dijital okuryazarlık ve teknostres düzeyleri, geçerliliği ve güvenilirliği kanıtlanmış ölçeklerle ölçülmüştür. Analizler Jamovi (2.3.28) aracılığıyla gerçekleştirilmiş ve bulguların öngörüsel sağlamlığı, KNIME’da Random Forest regresyon ile (R², MAE, RMSE) değerlendirilmiştir. Sonuçlar, öğrencilerin dijital okuryazarlık düzeylerinin yüksek, teknostres düzeylerinin ise düşük olduğunu göstermiştir. Doğrusal regresyon analizi, dijital okuryazarlığın teknostresi anlamlı biçimde ve olumsuz yönde yordadığını ortaya koymuştur. Ayrıca, dijital okuryazarlık ve teknostres düzeylerinin öğrencilerin eğitim aldığı bölümlere göre anlamlı biçimde farklılaştığı, uzaktan eğitim deneyiminin ise teknostres ortalaması üzerinde anlamlı bir etkisinin olmadığı saptanmıştır. Öngörüsel sağlamlık analizi kapsamında kurulan Random Forest modelinde, demografik değişkenlerin dâhil edilmesiyle tahmin performansı iyileşmiştir. Araştırma, dijital okuryazarlık ile teknostres arasındaki ters yönlü ilişkiyi vurgulamakta ve yükseköğretimde dijital eğitim politikaları ile stres yönetimi stratejilerinin geliştirilmesine yönelik öneriler sunmaktadır.

Keywords

• Teknostres
• Dijital Okuryazarlık
• Makine Öğrenmesi

Ethical Statement

Ufuk Üniversitesi Sosyal ve Beşeri Bilimler Bilimsel Araştırma Etik Kurul Başkanlığı'nın 20 Ağustos 2025 tarihli ve E-81182178-605-56402 sayılı Etik Kurul Onayı alınarak araştırma yapılmıştır.

The Examination of The Relationship Between Digital Literacy and Technostress in Online Courses: Statistical Analysis and Random Forest Machine Learning Predictive Robustness Assessment

Abstract

This study aims to examine the relationship between digital literacy and technostress among university students attending online courses and to assess the findings in terms of predictive robustness using a Random Forest–based machine learning approach.The study was conducted with 385 students enrolled in distance education programs at a private (foundation) university in Ankara. Participants’ digital literacy and technostress levels were measured using scales with established validity and reliability. Statistical analyses were performed using Jamovi (2.3.28), and the predictive robustness of the results was evaluated in KNIME via Random Forest regression using R², MAE, and RMSE. The results indicated that students’ digital literacy levels were high, whereas their technostress levels were low. Linear regression analysis showed that digital literacy significantly and negatively predicted technostress. In addition, both digital literacy and technostress levels differed significantly across academic departments, while prior distance learning experience had no significant effect on mean technostress. In the predictive robustness analysis, the Random Forest model’s predictive performance improved when demographic variables were included. The study highlights the role of digital literacy in reducing technostress and provides actionable recommendations for developing digital education policies and stress management strategies in higher education.

Keywords

• Technostress
• Digital Literacy
• Machine Learning

Ethical Statement

Ufuk University Social and Human Sciences Scientific Research Ethics Committee approval dated 20 August 2025 and numbered E-81182178-605-56402 was obtained, and the study was conducted accordingly.

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