Competency-Based Learning Object Sequencing Using Particle Swarms

In e-learning initiatives, sequencing problem concerns arranging a particular set of learning units in a suitable succession for a particular learner. Sequencing is usually performed by instructors, who create general and ordered series rather than learner personalized sequences. This paper proposes an innovative intelligent technique for learning object automated sequencing using particle swarms. E-learning standards are promoted in order to ensure interoperability. Competencies are used to define relations between learning objects within a sequence, so that the sequencing problem turns into a permutation problem and AI techniques can be used to solve it. Particle Swarm Optimization (PSO) is one of such techniques and it has proven with good performance solving a wide variety of problems. An implementation of the PSO, for learning object sequencing, is presented and its performance in a real scenario is discussed.

1. Introduction

Brusilovsky [1] envisages Web-based adaptive courses and systems as being able to achieve some important features including the ability to substitute teachers and other students support, and the ability to adapt (and so be used in) to different environments by different users (learners). These systems may use a wide variety of techniques and methods. Among them, curriculum sequencing technology “is to provide the students with the most suitable individually planned sequence of knowledge units to learn and sequence the learning tasks … to work with”. These methods derive from adaptive hypermedia field [2] and rely on complex conceptual models, usually driven by sequencing rules [3], [4]. E-learning traditional approaches and paradigms, that promote reusability and interoperability, are generally ignored, thus resulting in (adaptive) proprietary systems (such as AHA! [5]) and non-portable courseware.