E-LEARNING PERSONALIZATION BY STUDENT’S
PSYCHOLOGICAL CHARACTERISTICS

The paper presents the process of developing Student Profile for undergraduate students by mapping students categories explored with Felder-Silverman’s ILS questionnaire to the appropriate value of the personalization vector XYZ, and by deriving vector's values from the acquired student’s answers on Preference test. Obtained values of XYZ vector presents the PeLCoM (Personalized eLearning Course Model) metadata providing recommendations for creating personalized eLearning experience, where:

• the X axis enables personalization from the aspect of contents and structure of curriculum, educational goals, curriculum volume, the level of difficulty of the curriculum and the domain of the curriculum.
• the Y axis enables personalization from the aspect of curriculum visualization and the type of presentation (mathematical-logical, linguistic, musical, visual etc.);
• the Z axis enables personalization from the aspect of sequencing teaching materials (and the syllabus) on the level of lessons by supporting different systems of program contents, and from the aspect of sequencing teaching materials that constitute a lesson (in a single lesson) by supporting the defining of different views to a lesson.

Three important aspects of student diversity are notified: diversity to the knowledge level and learning objective, diversity to the learner’s behavior and diversity to the learning modalities and learner’s preferences. They are based on possible inductions of learning theories, learning strategies, cognitive styles, learning styles and the theory of multiple intelligences to the educational process. We present the mapping of these influential factors expressed by student’s psychological characteristics (students influence), to the pedagogical processes (teachers influence). Analyzing coordination between student’s learning style and his/her other preferences for specific teaching material, we generate the Student Profile and personalize eLearning experience according to characteristics memorized in profile.

Further, we describe how personalization system INDeLER (INDividualized eLEaRning) includes teacher's influence to the eLearning experience by composing different pedagogical aspects for Programming in C++ course. Sequencing algorithm based on student’s profile, composes the learning plan and for each learning unit generates the personalized eLearning sessions by designing lesson content, lesson’s presentation way and lesson’s visualization.

The example of INDeLER personalization process is also shown. We observe that student’s attention, motivation and engagement are increased in spite of (towards) classical lectures and multimedia animation inducts deeper understanding and faster relating of the learning material. We are evaluating eLearning personalization by INDeLER system to examine the efficiency of the proposed personalization method.

We have noticed that proposed personalization method approximately increase the student’s final results for 9% if the student is excellent, for 30% if the student is good, and have the minor effects if the student is bed, where the classification excellent, good or bed is defined according to the characteristics from Student Profile.

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