Measuring Students' Instantaneous Attention Levels in Classroom Environment: Development and Evaluation of Artificial Intelligence Software - Dikmen, Semih; Bahçeci, Ferhat (Kafkas Üniversitesi, 2025)
This study aimed to develop and implement an artificial intelligence (AI) software to measure students' attention levels in real-time during class. Additionally, the research explored instructors' perspectives on the use of AI for monitoring attention in the classroom. The quantitative phase of the research was conducted during the fall semester of 2021-2022 with 396 students from the Forensic Informatics Department at Fırat University. The qualitative phase involved five instructors participating in the software application. In the quantitative phase, data were collected using the AI software, an attention control scale, and an attention labeling form completed by three experts. A semi-structured interview form was used for the qualitative phase. The AI algorithm was developed by utilizing publicly available online photos to classify students' attention levels. Results showed that the AI software achieved a 97.2% accuracy rate in classifying attention levels. A pilot study conducted with 10 students provided strong evidence that the AI software could accurately measure attention levels. In the final phase involving 396 students, the AI software demonstrated its capability to visually report students' real-time attention levels by tracking head positions. The findings indicate that AI software can effectively monitor and report students' attention levels to instructors during class, providing valuable feedback in real time.
Keywords: Curriculum and Instration (Other); Eğitim Programları ve Öğretim (Diğer)