An Adaptive Time Management System for Student Learning

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Description/Abstract

We present a modular framework for an adaptive, position-aware student time management system, and a prototype imlementation distributed between a desktop PC and a PDA. The system uses an adapted version of Soloman & Felder's Index of Learning Styles questionnaire to determine the student's learning style. This is matched with the teaching style of module, acquired by using a complementary teaching style questionnaire, to create an individual study plan for a user-defined learning task hierarchy. Based on user feedback the schedule is continually adapted using a multi-layered neural network. The mobile part of the system uses GPS data to launch position-related reminders. The novelty of our approach is its comprehensiveness, combining aspects of education theory, time management, machine learning, and position-awareness in a single framework. Remaining work includes the integration into the university IT infrastructure and a thorough evaluation by a representative group of students.