An Adaptive Time Management System for Student Learning
An Adaptive Time Management System for Student Learning
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.
Students, Learning Management Systems
5357-5366
Rebenich, Till
c1823f89-b795-44ed-be3c-b11cfda30bba
Gravell, Andrew M
f3a261c5-f057-4b5f-b6ac-c1ca37d72749
30 June 2008
Rebenich, Till
c1823f89-b795-44ed-be3c-b11cfda30bba
Gravell, Andrew M
f3a261c5-f057-4b5f-b6ac-c1ca37d72749
Rebenich, Till and Gravell, Andrew M
(2008)
An Adaptive Time Management System for Student Learning.
World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008, Vienna, Austria.
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Conference or Workshop Item
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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.
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Gravell&Rebenich2008.pdf
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Published date: 30 June 2008
Additional Information:
Event Dates: 30 June 2008
Venue - Dates:
World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008, Vienna, Austria, 2008-06-30
Keywords:
Students, Learning Management Systems
Organisations:
Web & Internet Science, Electronic & Software Systems
Identifiers
Local EPrints ID: 266757
URI: http://eprints.soton.ac.uk/id/eprint/266757
PURE UUID: 9b204d53-7f38-49a6-b154-08ba865675ca
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Date deposited: 21 Oct 2008 11:58
Last modified: 14 Mar 2024 08:35
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Contributors
Author:
Till Rebenich
Author:
Andrew M Gravell
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