Abstract:
The "Eye Tracker for Formative Assessment" system represents a significant advancement in the realm of educational assessment. Traditional methods of assessing students’ comprehension levels often fall short in accurately gauging question difficulty and providing timely feedback to educators. To address this challenge, our project introduces a novel approach that leverages eye-tracking technology integrated with mobile devices to monitor students’ eye movements during in-class formative assessments. Through the analysis of eye movement patterns, our system aims to provide educators with real-time insights into the difficulty levels of questions posed to students. By employing machine learning algorithms, we develop a predictive model capable of accurately assessing question difficulty based on students’ eye movements. This real-time feedback empowers educators to adapt their teaching methods on-the-fly, leading to more effective and equitable formative assessments in undergraduate education. Key components of our project include the design of a user-friendly mobile application, rigorous testing and validation procedures, ethical handling of data, scalability considerations, iterative improvement strategies, and knowledge dissemination efforts. By addressing the critical need for objective and timely assessment of question difficulty, our project seeks to enhance the overall quality of undergraduate education and promote student success. i
