Today’s e-learning is dominated by the Learning Management Systems (LMS), which offer support for a wide area of activities in the e-learning process (i.e. teachers can use LMS for the creation of courses and test suites, for communicating with the students, for monitoring and evaluating their work; students can learn, communicate and collaborate by means of LMS). The problem is that LMS don’t offer personalized services, all the students being given access to the same set of educational resources and tools, without taking into account the differences in knowledge level, interests, motivation and goals. Adaptive educational hypermedia systems (AEHS) try to offer an alternative to this non-individualized instruction approach, by providing various services adapted to the learner profile. The purpose of this adaptation is to maximize the subjective learner satisfaction, the learning speed (efficiency) and the assessment results (effectiveness).

The focus of our work is on the learning style as the adaptation criterion, since it is one of the individual differences that play an important role in learning, according to educational psychologists. Learning style refers to the individual manner in which a person approaches a learning task. For example, some learners prefer graphical representations and remember best what they see, others prefer audio materials and remember best what they hear, while others prefer text and remember best what they read. There are students who like to be presented first with the definitions followed by examples, while others prefer abstract concepts to be first illustrated by a concrete, practical example. Similarly, some students learn easier when confronted with hands-on experiences, while others prefer traditional lectures and need time to think things through. Some students prefer to work in groups, others learn better alone. These are just a few examples of the many different preferences related to perception modality, processing and organizing information, reasoning, social aspects etc, all of which can be included in the learning style concept.

WELSA overview

The main pedagogical goal of WELSA is to provide an educational experience that best suits the learning style of each student. In order to do that, the system first needs to identify these learning preferences. The learning style model employed in WELSA is called ULSM (Unified Learning Style Model). ULSM is actually a collection of learning preferences extracted from the main learning style models proposed in the literature, which was conceived to cover a wide range of characteristics, while at the same time aiming for independence between the learning preferences and the least possible overlap.

Regarding the method used for the identification of the students’ preferences, an implicit approach was chosen, based on the analysis of the students’ interactions with the system. This overcomes the psychometric flaws of the traditional measuring instruments, while at the same time is more user friendly, not requiring any additional work from the part of the student. Furthermore, with this intelligent modeling mechanism, the learner model may be continuously updated (i.e. dynamic modeling method).

As far as the adaptation technologies are concerned, WELSA makes use of both adaptive presentation and adaptive navigation support technologies, providing the student with an individualized path through the learning material. The process is fully automated, based on a set of built-in adaptation rules: the course pages are dynamically generated by the system for each student, according to her/his learner model.

WELSA is composed of three main modules:

  • an authoring tool for the teachers, allowing them to create courses conforming to the internal WELSA format (XML-based representation)

  • a data analysis tool, which is responsible for interpreting the behavior of the students and consequently building and updating the learner model, as well as providing various aggregated information about the learners

  • a course player (basic learning management system) for the students, enhanced with two special capabilities: i) learner tracking functionality (monitoring the student interaction with the system); ii) adaptation functionality (incorporating adaptation logic and offering individualized course pages).

As far as the implementation is concerned, Java-based and XML technologies are employed for all WELSA components. Apache Tomcat 6.0 is used as HTTP Web server and servlet container and MySQL 5.0 is used as DBMS.

Research framework

The work was carried out in the context of a national research grant financed by the Romanian Ministry of Education and Research – CNCSIS (Study, design and implementation of an adaptive intelligent web-based system for e-learning, grant CNCSIS TD, code 167, duration: 2007-2008, grant director: Elvira Popescu). More details about this project can be found here.

This work represents a part of the PhD thesis "Dynamic adaptive hypermedia systems for e-learning", finalized and defended in 2008 (author: Elvira Popescu, supervisors: Prof. dr. Vladimir Rasvan – University of Craiova, Romania and Prof. dr. Philippe Trigano, Université de Technologie de Compiègne, France).