Title: GOALS: generator of adaptive learning scenarios
Authors: Karim Sehaba; Aarij Mahmood Hussaan
Université de Lyon, CNRS, Université Lyon 2, LIRIS, UMR5205, F-69676, France
Department of Computer Science, IQRA University, Karachi, Pakistan
Abstract: The problem of generating personalised learning activities for learners is a difficult task. The difficulty is compounded if the learning activity is mediated or presented through a serious game. In this paper, we present a system, called generator of adaptive learning scenarios (GOALS) that is capable of generating learning scenarios taking into account the learners' interaction traces, pedagogical objectives and the specificities of serious games. The generator we propose aims to be generic, i.e., independent of the application domain and serious games. To achieve this, we propose an architecture that organises the knowledge in three layers: domain concepts, pedagogical resources and serious game resources. This work has been conducted in the context of Cognitive Linguistic Elements Stimulations Project (CLES). This project targets the development of an online serious game dedicated to persons with cognitive disabilities. In order to validate our approaches, we conducted experiments in the context of CLES project. These experiments are based on comparative method that compares the results generated by our system with that of an expert. The results of the valuations, conducted with a domain expert and end users, are also presented.
Keywords: serious games; adaptive generator; learning scenarios; cognitive disabilities; interaction traces; adaptive learning; personalised learning; online games; personalisation.
Int. J. of Learning Technology, 2013 Vol.8, No.3, pp.224 - 245
Available online: 04 Oct 2013