Automatic Learning Object Selection and Sequencing in Web0Based Intelligent Learning Systems

Abstract

Automatic courseware authoring is recognized as among the most interesting research questions in intelligent Web-based education. Automatic courseware authoring is the process of automatic learning object selection and sequencing. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection and sequencing are based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e., domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make “instructional sense.” Moreover, in order to design adaptive learning systems, a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners are rather complex. In this chapter, we address the learning object selection and sequencing problem in intelligent learning systems proposing a methodology that, instead of forcing an instructional designer to manually define the set of selection and sequencing rules, produces a decision model that mimics the way the designer decides, based on the observation of the designer’s reaction over a small-scale learning object selection case.

A System for E-Learning Processes Management

“…Further, an addition of questionnaires might be implemented or uploaded to internet for the purpose of supporting decision makers evaluate study and know about opinions of all users, students and trainees and try to eliminate obstacles and difficulties they might be exposed to. A wide and strong set of rules of dependencies among users, methods and learning objects is needed to describe these eight types of adaptation and moreover their possible combinations (Karampiperis and Sampson, 2004). On the other hand, the categories in the proposed system are originally concentrated.…”

“…The electronic learning system is evaluated for three main components: The usability evaluation to cover the graphical user interface, easy to use, helpfulness and alertness; the fulfillment evaluation to examine the features incorporated in terms of reusability, interoperability, durability and accessibility and the overall satisfaction (Ganchev et al, 2007). A wide and strong set of rules of dependencies among users, methods and learning objects is needed to describe these eight types of adaptation and moreover their possible combinations (Karampiperis and Sampson, 2004). We define adaptive E-learning as a method to create a learning experience to the student, but also to the tutor, based on the configuration of a set of elements in a specific period aiming to increase of the performance of a pre-defined criteria (Van Rosmalen et al, 2006).…”

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

“…Current methods for personalization of learning can be divided into three groups: (i) oriented activities approaches [3]: where the learning process is represented by a graph in which the activities are identified and decomposed. (ii) Oriented resources approaches ( [4], [5], [6]): in which the learning process returns to select, assemble and present contents, (iii) oriented objectives approaches ( [7], [8]) : in which case the the learning process is seen as a process of satisfaction of pedagogical objectives already defined. These approaches use a set of algorithms and techniques from Artificial Intelligence and Web Semantics known as ant colony optimization ( [9], [10], [11], [6]), Bayesian networks [7], the algorithm of Support Vector Machines (SVM) [12], ontologies [13].…”

Designer - Supporting Teachers Experience in Learning Management Systems

“…In [3], [12] Karampiperis and Sampson proposed an approach based on a knowledge ontology, learning object metadata and competences which uses a weighted shortest path algorithms to generate an optimum learning path.…”