Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences

Eye tracking technology in adaptive learning systems enhances diagnostic capabilities by providing valuable insights into cognitive processes. This information can be leveraged to identify and address difficulties. So far, there have been only few attempts of realizing this. Studies are usually only about recognizing correctness of answers and the evaluation is complex and difficult to transfer due to features depending on Areas of Interests (AOIs). We close this gap and present a time-dynamic approach to identify specific difficulties based on raw gaze data. The eye tracking data of 139 students while solving a math problem serve as a sample. Difficulties that arose during the solution process are known. A temporal convolutional network (TCN) is trained to perform a multiclass classification on sequential data. On this basis we present an algorithm which simulates a dynamic classification in an adaptive real-time system. We evaluate this procedure achieving an accuracy of almost 80%.

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Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences

Cited by 2 publications

References 12 publications

Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep Learning

Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep Learning

Real-time Prediction of Students' Math Difficulties using Raw Data from Eye Tracking and Neural Networks

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