Abstract:

Nowadays research results have been obtained to provide virtual learning environments that can offer educational content, in the form of learning objects, such as videos, simulations, animations, games and others, appropriate to the student's profile and allowing a ludic and effective learning.

These learning objects are cataloged in repositories according to international standards to facilitate their classification, retrieval and reuse.

Some researches around the world have studied and implemented systems that offer solutions considering as one possible adaptation criterion the student's learning styles.

In this sense, this paper presents a simulator that implements an algorithm that extends the solutions presented in the systems reported in the literature and provides a selection of learning objects to the AdaptMLearning architecture. It takes into account as adaptation criteria the following data: the mobile device's technological specification; the student's learning style information, his/her the performance and timing associated to the student's interaction with the learning object; previously acquired knowledge by the student related to the course's content. In addition, it also allows the teacher to interfere in the adaptation criteria used during the study simulation, and allows the student to indicate his/her preferences for media types.

More specifically, the student can inform his/her preference about media types, such as: simulation, animation, video, audio, game and text, allowing the selection of the most appropriate learning object according to the indicated preference.

From the technological point of view, the AdaptMLearning architecture does not restrict the student's access device, i. e., he/she can use any kind of device: Smartphone, tablet, notebook or personal computer, with different keyboard types and screen sizes.

The initial student's learning styles has been modeled using the ILS (Index of Learning Styles) of FSLSM (Felder and Silverman Learning Styles Model). It was considered that the student's style can change. This characteristic has been considered by a fuzzy modeling of the problem that takes the student's performance and study duration information as input variables.

The IEEE 1484.12.1, used in SCORM and IMS Learning Design, was chosen to describe a learning object.

The simulator was designed using UML modeling and implemented using Java, JFuzzyLogic and MySQL.

The simulator has been evaluated and validated using a quite comprehensive set of scenarios and it proved its usefulness for the planning and design of virtual learning environments using the AdaptMLearning.

Keywords:

AdaptMLearning, Adaptability, Learning Objects, Learning Styles, Performance, Technology.