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Title: PASER: A Curricula Synthesis System based on Automated Problem Solving
Author(s): D. Vrakas, G. Tsoumakas, F. Kokkoras, N. Bassiliades, I. Vlahavas, D. Anagnostopoulos.
Availability: Click here to download the PDF (Acrobat Reader) file (13 pages).
Keywords: Curricula Synthesis, Automated Planning, Text Mining.
Appeared in: International Journal on Teaching and Case Studies, Special Issue on "Information Systems: The New Research Agenda, the Emerging Curriculum and the New Teaching Paradigm", Inderscience, Vol. 1, Nos. 1/2, pp. 159-170, 2007.
Abstract: This paper presents PASER, a system for automatically synthesizing curricula using AI Planning and Machine Learning techniques on an ontology of educational resources metadata. The ontology is a part–of hierarchy of learning themes which correspond to RDCEO competencies. The system uses an automated planner, which 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.) constructs a complete educational curriculum that achieves the goals. PASER is accompanied by a Machine Learning module that classifies textually described users’ learning requests to competencies registered within the ontology. Furthermore, the ML module interactively assists content providers in constructing educational resources metadata (LOM records) that comply with the ontology concerning both learning objectives and prerequisites.
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