University Mediterranean (MONTENEGRO)
About this paper:
Appears in: EDULEARN12 Proceedings
Publication year: 2012
Pages: 2252-2261
ISBN: 978-84-695-3491-5
ISSN: 2340-1117
Conference name: 4th International Conference on Education and New Learning Technologies
Dates: 2-4 July, 2012
Location: Barcelona, Spain
There are many factors that can influence the extent of learning. These would include factors such as the student’s learning style, motivation for learning, decisions about relevant learning resources and consequently, dynamics of learning and teaching. Many of mentioned factors are inter-correlated and recent research is mostly focused on retrieval of relevant learning materials adaptive to the capabilities of learner and available resources. The adaptive e-learning system is typically presented with a block diagram, which consists of the domain ontology, student model, adaptive retrieval module and the learning object repository. Student model is seen as an abstract entity and the student profile represents an instantiation of the student model for a particular user. It integrates his/her personal preferences and in the literature, many different strategies are adopted for providing the personalized information. The adaptivity of such systems are mainly focused on allocation of available resources based on general requirements, about further and additional learning of segments of lectures that student previously did not adequately learn and understand.

In this paper we put the attention on the teachers who define different kinds of requirements describing the conditions and circumstances which the selection of appropriate learning resources depends on. They may integrate a variety of different information and characteristics, contained either in a student model, or even in any other part of a system (e.g. environmental characteristics such as time features, etc). For example, teacher may define requirement in the form: if a student did not successfully master the previous lectures and there is only few days left to the exam, student should be concentrated on the basic concepts of the whole course. Otherwise, if there is more time for preparation, basic concepts with additional practical exercises should be dedicated to the student. These conditional requirements define the recommendation of resources dynamically in relation to the most characteristics of student model on one side, and available resources, on another. More precisely, even if the teacher defines requirements about the most suitable plan of learning guidance, the second phase includes findings of the most appropriate learning resources related to teachers’ requirements and potentially defined students’ preferences in his profile. Thus, the elicitation and processing of such requirements enable dynamical construction of learning resources adaptive to the students, available resources and learning environment characteristics.

The representation of preferences and its processing has been studied in many fields such as economics, especially in project and risk management, decision theory, social choice theory, with further developments and applications in areas such as operational research, databases, security analysis, and artificial intelligence. The main goal of this paper is to analyze the possibilities of their integration in the learning environment for the both participants in the learning process, i.e. students and teachers. The availability of automated analyzes of defined requirements allows for more subtle personalization and dynamically allocated higher quality recommendation.
Dynamic, adaptive, e-systems, requirements, recommendation, learning resources.