A Learning Style Identification Approach in Adaptive E-Learning System

The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.

Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (r<sub>s</sub>=.276, p=.000), but statistically significant (p<0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.

After conducting a historical review and establi-shing the state of the art of the various approaches regarding the design and implementation of adaptive e–learning systems—taking into consideration the characteristics of the user, in particular their learning styles and preferences in order to focus on the possibilities for personalizing the ways of utilizing learning materials and objects in a manner distinct from what e–learning systems have traditionally been, which is to say designed for the generic user, irrespective of individual knowledge and learning styles— the authors propose a system model for the classification of user interactions within an adaptive e–learning platform, and its analysis through a mechanism based on backpropagation neural networks and fuzzy logic, which allow for automatic, online identification of the learning styles of the users in a manner which is transparent for them and which can also be of great utility as a component of the architecture of adaptive e–learning systems and knowledge-management systems. Finally, conclusions and recommendations for future work are established.

Initiatives based on learning style adaptation are often marked by a lack of experimental evaluation of their efficacy in general and of student satisfaction in particular. A high level of satisfaction can increase the motivation of students, their engagement and experience, and therefore improve their learning. This paper is concerned with the investigation of the effect of adaptation based on learning style on students' satisfaction. An adaptive approach, which involves the construction of personalised learning paths based on learning style, was implemented in an adaptive e-learning system. A controlled experiment in this learning environment was conducted with sixty undergraduate students to evaluate their level of satisfaction with the system. The results indicate that the students were generally satisfied with their learning experience. It can be concluded that adaptation of learning content according to learning style can improve student satisfaction and enhance their experience and motivation.

The domain of education has taken great leaps by capitalizing on technology and the utilization of modern devices. Nowadays, the established term "one size fits all" has begun to fade. The research focuses on personalized solutions to provide a specially designed environment on the needs and requirements of the learner. The adaptive platforms usually use Learning Styles to offer a more effective learning experience. This review analyzes the learner model, adaptation module, and domain module, originating from the study of 42 papers published from 2015 to 2020. As more modern techniques for adaptation get incorporated into e-learning systems, such techniques must be compliant with educational theories. This review aims to present the theoretical and technological background of Adaptive E-learning Systems while emphasizing the importance and efficiency of the utilization of Learning Styles in the adaptive learning process. This literature review is designated for the researchers in this field and the future creators and developers of adaptive platforms.

Adaptive education systems are considered one of the most interesting research issues in technology-based learning strategies. Since learners have different needs, abilities and learning preferences, we should fit the curriculum and teaching strategies to these different learning styles. This article investigates the impact of using LAES (Libyan Adaptive Education System) on the performance of learners. An ALSI (Arabic Learning Style Instrument) was integrated into the LAES system to consider learner preferences. Results obtained by the learners in the ALSI instrument were used to initialize their profiles. And then, an experimental study was conducted to investigate the impact of LAES system on the performance of learners. The results reveal the students who have learnt using LAES system were better than others who have learnt without the LAES in terms of the knowledge gained.

These days modifications and supplements to e-learning are not enough to make them successful since flexibility and adaptability is missing. Different e- Learning architecture provide different kinds of learning experience to the learners. The lack of adaptive system is due to ―One size fits for all‖ concept. Currently the research is towards to learner oriented platforms putting student motivation, habits, expectations and learning styles (additional Koper, R.; Burgos, D (2005)). The concept of adaptation has become a prominent issue in e- learning. Systems allow the users to change some system parameters and adapt their behavior accordingly is known as adaption process. Systems that adapt based on the systems assumption are called as adaptive systems (additional santally mohammed issack). Since the system adapts to a person and personalizes the learning process it can be also known as personalized system (additional Paramythis, A.; Loidl-Reisinger). So to serve the learning community better there was a necessity to profile them and identify their requirements and provide an adaptive e- learning architecture which suits the individuals needs better. So the main objective of this research was to provide an adaptive e-learning system architecture which satisfies Felder Silverman learning style. Felder- Silverman's learning model (FSLM) was chosen since FSLM synthesis of these theories can be designed to easily translate them into strategies into a university setup. Index Terms—Learning, e-Learning, Virtual Learning, e- Learning Standards, Adaptive e-learning systems.

This paper introduces a complex plan for a complete system of individualized electronic instruction. The core of the system is a computer program to control teaching, the so called “virtual teacher”. The virtual teacher automatically adapts to individual student’s characteristics and their learning style. It adapts to static as well as to dynamic characteristics of the student. To manage all this it needs a database of various styles and forms of teaching as well as a sufficient amount of information about the learning style, type of memory and other characteristics of the student. The information about these characteristics, the structure of data storage and its use by the virtual teacher are also part of this paper. We also outline a methodology of adaptive study materials. We define basic rules and forms to create adaptive study materials. This adaptive e-learning system was pilot tested in learning of more than 50 students. These students filled in a learning style questionnaire at the beginning of the study and they had the option to fill in an adaptive evaluation questionnaire at the end of the study. Results of these questionnaires were analyzed. Several conclusions were concluded from this analysis to alter the methodology of adaptive study materials.

Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model

Application of a fuzzy controller in adaptive e-learning content used to evaluate student activity

Various types of learners can be observed in today’s e-learning environments. Learning Analytics can offer insights on a student’s actions and behaviour. We can correlate this data with the identification of learning styles like in the Felder-Silverman Learning Styles Model (FSLSM), and use it for user modelling. Supported by visualizations on learning styles and learning behaviour students and instructors can reflect the learning process. Based on our previously published conceptual model for linking learning analytics and learning styles in e-learning environments, we present an Integrated Learning Styles Analytics system (ILSA), which supports identification of learning styles as well as analysis and visualization of activity data in the Moodle LMS. ILSA consists of a questionnaire for learning style identification derived from FSLSM and offers immediate results to its participants. Next, by utilizing various data sources in Moodle, e.g. log data or the grade book, we are able to correlate a user’s activity with their learning styles. This work offers details on the finalized concept as well as its implementation. By providing insights on data sources in Moodle and presenting various visualizations, this work allows teachers to reuse the system in their e-learning courses.

rpose: This study aims to develop and evaluate an adaptive e-learning model tailored to the diverse learning styles of students, particularly within the context of distance learning at STIE Krakatau. The objective is to overcome the limitations of conventional e-learning systems that deliver uniform content to all users, regardless of their individual learning needs. Methodology: The study employed a development-based approach using an engineering model consisting of analysis, design, implementation, and evaluation phases. The adaptive e-learning system was created using multimedia authoring tools and tested in a seminar and training session lasting three hours. Results/Findings: The results demonstrated that the adaptive e-learning system effectively tailored instructional materials to the students' preferred learning styles (visual, auditory, and kinesthetic). The system successfully identified students’ learning preferences through a questionnaire and adjusted content presentation accordingly. Ongoing evaluations confirmed that the system functioned as intended and aligned with the designed functionalities. Conclusions: The implementation of an adaptive e-learning model significantly enhances the personalization and relevance of instructional materials, leading to better engagement and potentially improved learning outcomes in distance education. The seminar and training program proved effective in introducing and applying this adaptive model in a higher education setting. Limitations: The study was limited to formative evaluations and alpha testing. Further testing, including beta testing and broader implementation, is needed to determine user acceptance and the system's effectiveness in real classroom settings. Additionally, the current version relies on self-reported learning styles which may affect the accuracy of adaptation. Contributions: This research contributes to the advancement of personalized e-learning technologies by proposing a dual-level adaptation model incorporating both adaptivity (system-driven) and adaptability (user-driven).

Adaptive e-learning systems enhance the efficiency of online education by providing personalised, adaptive contents and user interfaces which change with respect to learner's requirements. In order to understand the learner's requirements, learners with similar learning behaviour have to be grouped into clusters based on the usage data of each learner. In this paper, a clustering technique to group learner's profiles is proposed where learners will be grouped based on similar sequences of accesses to learning material and time spent. A learner's model is designed based on Felder and Silverman learning style model. The clustering algorithm has two different phases, where the first phase considers the all sequences of access of learners which are in the chronological order of accessing the learning components and learning materials on the portal. The second phase considers the time spent on each learning components as a fuzzy membership function and groups the similar sequences of learners into three clusters. Learners in the clusters have similar learning behaviour for providing adaptive interfaces and contents.

Improving online education through automatic learning style identification using a multi-step architecture with ant colony system and artificial neural networks

Automatic detection of learning styles for an e-learning system