Adaptive Learning

• Main Author: Grundtman, Per
• Format: Others
• Language: English
• Published: Luleå tekniska universitet, Institutionen för system- och rymdteknik 2017
• Subjects: adaptive learning; machine learning; e-learning; biosyncing; biometric sensors; Empatica E4; Intelligent Tutoring Systems; WEKA; Computer and Information Sciences; Data- och informationsvetenskap; Engineering and Technology; Teknik och teknologier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648

The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.