Studying the implications of hidden learning styles by tracing learners' behaviors in an eLearning system

eLearning as defined by George Siemens is "The marriage of technology and education" (Siemens G., 2002). eLearning provides just in time anywhere training which saves learners a lot of time, costs and effort. eLearning enjoys a major competitive advantage over the traditional classroom as it provides direct access to each and every learner and so it is capable of offering a one to one tutoring environment. eLearning can provide an individualized adaptive learning experience based on students' learning styles unlike the traditional classroom scenario where the instruction has to be adjusted to suit the average level of the attendees, but what is meant by Learning styles? Learning styles are defined as "the ways in which an individual characteristically acquires, retains, and retrieves information" (Felder R. & Henriques E., 1995). Each learner has his/her own preferred way that s/he uses to learn based on different factors such as heredity, educational background, professional background, age and context. Learners prefer to learn in many ways like seeing, hearing, reflecting, visualizing, discussing with peers, doing or logical reasoning. The instructional or teaching methods employed by tutors may also vary. Some tutors prefer to speak and lecture all the time, others prefer to get engaged with the students in open discussions to elaborate more on the topic under study, others demonstrate the topic under study using a chart or a model, while others encourage students to learn by doing.;Nowadays, most eLearning systems are merely based on fixed material content and presentation that are previously set by the tutor, not taking into account the learning styles of learners; so the learner has to learn the course in a specific sequence previously designed by the tutor. In other words, the static content is presented in a one-for-all or so called one-size fits them all scenario ignoring the different learners' learning styles. This has led to low retention ratio as it fails to motivate learners who feel uncomfortable, inattentive, discontent and bored. They gradually lose interest and get discouraged about the course and finally they easily make their "stay or go" decision and abandon the online courses they were registered for. Some systems even try to accommodate the different learning styles by offering comprehensive material with different types and different forms but still this is considered as a one-for-all design because all learners are exposed to the same interface and same material (Aase M., 2002).;Almost all current adaptive eLearning systems force learners to fill in tedious questionnaires in order to identify their learning styles. eLearning systems are faced with the challenge to become more adaptive in a smart way. The objective of this research is to develop an eLearning system that identifies the relationship between the learners behavior patterns and their hidden learning styles. The system was given the name YMAS which stands for the initials of my beloved family "Yakout", "Mona", "Ahmed" and "Sherif". And for future work YMAS will be modified to dynamically identify the learner's learning style by simply observing and capturing his/her browsing behavior without requesting him/her to answer any questionnaires.;YMAS will be developed to capture information about students' behaviors on the system filling in values for predefined parameters affected by such behavior, at the same time students will be requested to fill in a questionnaire that was designed and developed by Dr. Felder Silverman and Dr. Barbra Soloman named "Index of Learning Styles" (ILS). The values for the different parameters together with the identified learning styles will be fed into a neural network. The neural network will be trained to identify the learning styles based on the variables' values. The parameters traced will be refined and validated. The research study aims at proposing a model which defines the different validated parameters that should be traced in order to identify learner's learning style. The model will also show the relationship between each and every parameter and each and every learning style's dimension. Information about the identified learning style will be communicated to the learner, by doing so YMAS has achieved two minor objectives which are increasing the learner's self awareness about his/her strengths and weaknesses as a learner and providing him/her with indications of skills that s/he should work on acquiring or improving in order to enhance his/her academic performance as well as building learner's repertoire of learning styles.