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On the automatic compilation of e-learning models to planning

Published online by Cambridge University Press:  08 February 2013

Antonio Garrido ,
Susana Fernández ,
Lluvia Morales ,
Eva Onaindía ,
Daniel Borrajo  and
Luis Castillo
Antonio Garrido
Affiliation:
Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46071 Valencia, Spain; e-mail: agarridot@dsic.upv.es, onaindia@dsic.upv.es
Susana Fernández
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain; e-mail: sfarregu@inf.uc3m.es, dborrajo@inf.uc3m.es
Lluvia Morales
Affiliation:
Departamento de Ciencias de la Computación e I.A., Universidad de Granada, C/ Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain; e-mail: lluviamorales@decsai.ugr.es, l.castillo@decsai.ugr.es
Eva Onaindía
Affiliation:
Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46071 Valencia, Spain; e-mail: agarridot@dsic.upv.es, onaindia@dsic.upv.es
Daniel Borrajo
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain; e-mail: sfarregu@inf.uc3m.es, dborrajo@inf.uc3m.es
Luis Castillo
Affiliation:
Departamento de Ciencias de la Computación e I.A., Universidad de Granada, C/ Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain; e-mail: lluviamorales@decsai.ugr.es, l.castillo@decsai.ugr.es
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Abstract

This paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domain-independent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: (i) hierarchical, (ii) including PDDL (Planning Domain Definition Language) actions with conditional effects and (iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Special Issue 2: Knowledge Engineering for Planning and Scheduling , June 2013 , pp. 121 - 136
Copyright
Copyright © Cambridge University Press 2013 

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