Abstract:

This paper will describe the development and implementation of an Adaptive System Prototype with the aim to manage an automated and customized learning experience.

The developed technology, follow the goal to identify any end user of the LMS, create a customized user profile with their starting skills and learning preferences in order to automatically tailor a personalized learning path. The main purpose is to maximize student’s performance.

This paper describes the main steps for the implementation of the prototype of this Adaptive Learning System named iO3 (intelligent Open Cube), as it will be an intelligent, Open Source, Open Knowledge, Open Plugin system.

Considering the state of the art of most common e-learning environments, many researchers believe that adaptive learning is a critical requirement to enhance the teaching quality and the user performance throughout the learning process. The adaptive learning feature improves the starting skills of a user, providing specific content related to his learning style.

The road to follow in order to build an effective learning and teaching performance cannot overlook the aspect of personalized learning environments.
Generating a customized learning path for any learner provides a challenging work for researchers involved, both, in informatics and education fields. Learning Path is a collection of different learning items that are combined to achieve a specific learning goal. Furthermore, the development of an customized (adaptive) learning path means that learner's starting skills should be evaluated for providing a tailored learning path suitable for each student. Different learners may have different characteristics, prior knowledge, starting skills, motivation or needs. This variety of "distinguishing marks" commonly requires the management of different information to different learners in a different format. Taking this in count, our research group believes that it is fundamental to develop adaptive educational systems which consider the several individualities of each student when presenting information, learning objects and/or practice opportunities, in order to make the learning process as effective, efficient, and motivating as possible. This is not a way to rethink education, but a way to reinforce opportunities of professional and educational growth.

The purpose of this research study is to identify a process to deliver the right contents according to the student’s learning style and to develop/implement it in a software module. On the methodological point of view, the main questions that this research project aims to answer are:
Which learning objects should be picked to build a learning path suitable for each user?
How could adaptive learning be implemented in Open Source LMS?
To answer these questions the research team investigates the adaptive technology and, in this paper, describes the implementation of an adaptive "strategy" in the mostly used LMS.

In the development approach proposed, user profile is assessed and evolved using Case Based Reasoning. The Adaptive Learning Path is generated using the learner's behaviour patterns, which are modelled as the learner's characteristics like learning styles, goals and performance.
In this personalized learning environment, the student’s performance will be enhanced and the expected result is to reach higher motivation to finish the course, avoid situation in which the student has to study un-useful or unsuitable contents.

Keywords:

Adaptive learning, Automatic Learning Path design, e-learning, Personalized Learning Environments.