PASER: a curricula synthesis system based on automated problem solving
by Dimitris Vrakas, Grigorios Tsoumakas, Fotis Kokkoras, Nick Bassiliades, Ioannis Vlahavas, Dimosthenis Anagnostopoulos
International Journal of Teaching and Case Studies (IJTCS), Vol. 1, No. 1/2, 2007

Abstract: This paper presents PASER, a system for automatically synthesising curricula using AI Planning and Machine Learning techniques based on an ontology of educational resources metadata. Given the initial state of the problem (learner's profile, preferences, needs and abilities), the available actions (study an educational resource, take an exam, join an e-learning course, etc.) and the goals (obtain a certificate, learn a subject, acquire a skill, etc.), the planning module of PASER constructs a complete educational curriculum that achieves the goals. The Machine Learning module of PASER matches textually described learning requests, objectives and prerequisites to concepts of the ontology.

Online publication date: Mon, 25-Jun-2007

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