Abstract
Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM). This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.
Highlights
In the past decade, Adaptive E-learning Systems (AESs) have attracted much attention of the researchers in the fields of Educational Data Mining (EDM)
While going through the online courses on e-learning portals, most of the learners are unsure of their actual needs which may lead to inaccurate requests of the learning contents
The use of personalized e-learning and adaptive e-learning system has become increasingly important in recent years with extensive research being devoted to finding different ways of tailoring the learning experience for individual students
Summary
Adaptive E-learning Systems (AESs) have attracted much attention of the researchers in the fields of Educational Data Mining (EDM). Many AESs have been proposed and implemented on Human Computer Interface (HCI) and Data Mining. Most of these AESs have focused on addressing the requirements of learners in order to improve the interaction and efficiency of the systems. While going through the online courses on e-learning portals, most of the learners are unsure of their actual needs which may lead to inaccurate requests of the learning contents. To address these issues, it is beneficial that e-learning system analyze the actual needs of learners to improve their learning performance [9]
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