Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ1The Strategic Use of Instructor Visibility in Educational Videos: An Eye-Tracking Data-Based Study
Türel, Yalın Kılıç; Alpsülün, Mustafa; Yuruk, Süleyman Eren; Aslan, Alper; Kokoç, Mehmet; Polat, Hamza; Öztürk Akca, Cemile; Kurşun, Engin
This study developed an exploratory heuristic decision-support framework for dynamically regulating instructor visibility in instructional videos based on learners’ eye-tracking data. Eye-movement data were collected from 40 university students using a Tobii Pro Fusion while viewing 16 explanatory and procedural instructional videos. Instructor and Learning Content Areas of Interest were analyzed using normalized eye-tracking indicators. Descriptive analyses were complemented by Wilcoxon signed-rank tests, binary logistic regression, and receiver operating characteristic (ROC) analyses. Learners allocated significantly greater visual attention to learning content than to the instructor across both video types. Logistic regression identified instructor attention percentage, visit-related metrics, and blink frequency as significant predictors of visibility decisions. ROC analysis showed that instructor attention percentage provided the highest discriminative performance, with an optimal threshold of approximately 30%. Findings suggest that instructor visibility may be particularly appropriate during introductory, summary, and conceptually demanding segments, providing empirically grounded, attention-aware guidance for adaptive instructional video interfaces.
- MakaleAçık Erişim2025Scopus SJRQ2Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting
Utku, Anıl; Can, Umit; Alpsülün, Mustafa; Balıkçı, Hasan Celal; Amoozegar, Azadeh; Pilatin, Abdulmuttalip; Barut, Abdulkadir
Particulate matter, particularly PM2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.
AtıfScopus10WoS6OpenAlex11
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