A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

A New Approach for Modeling and Discovering Learning Styles by Using Hidden Markov Model

Adaptive learning systems are developed rapidly in recent years and the heart of such systems is user model User model is the representation of information about an individual that is essential for an adaptive system to provide the adaptation effect i e to behave differently for different users There are some main features in user model such as knowledge goals learning styles interests background but knowledge learning styles and goals are features attracting researchers attention in adaptive e-learning domain Learning styles were surveyed in psychological theories but it is slightly difficult to model them in the domain of computer science because learning styles are too unobvious to represent them and there is no solid inference mechanism for discovering users learning styles now Moreover researchers in domain of computer science will get confused by so many psychological theories about learning style when choosing which theory is appropriate to adaptive syste

On the way to learning style models integration: a Learner's Characteristics Ontology

[EN] On the way to increasing customization in e-learning systems, the learner model is the main source of variability. Such a model includes a number of psychological characteristics and study preferences that describe the learner's personality traits related to learning. During the last decades, the design methods and tools for e-learning have been designed assuming speci fi c learner models. Therefore, in the search for a learning environment suitable for as many learner models as possible, we need tools to explore -and exploit- such models. In general, the learner's characteristics can be linked to the so-called learner's learning style (which is a part of the learner model) to provide the instructor with extensive knowledge about the learner's characterization in perceiving and processing information. Numerous learning styles have been proposed in the last decades, in some cases with overlapping characteristics with the same or different names. Thus, the heterogeneity of the learning style space makes it dif fi cult to handle cus- tomization effectively. In this paper, we introduce a Learner's Characteristics Ontology based on creating interconnections between the different learning style model dimensions and learning styles with the relevant learner's characteristics, that: (1) helps instructors to improve and personalize the learning content; (2) can recommend learning materials to learners according to their learning characteristics and preferences; (3) can provide both instructors and learners with extensive knowledge about how they can improve their teaching and learning abilities; and (4) can improve communications and interaction between humans and computers by specifying the semantics of the learning style models' characteristics.The work of J. H. Canos and M. C. Penades is funded by the Spanish MINECO under grant CALPE (TIN2015-68608-R).Ezzat Labib-Awad, A.; Canos Cerda, JH.; Penadés Gramage, MC. (2017). On the way to learning style models integration: a Learner's Characteristics Ontology. Computers in Human Behavior. 73:433-445. https://doi.org/10.1016/j.chb.2017.03.054S4334457

Location-Based Learning Management System for Adaptive Mobile Learning

E-learning and distance learning are all forms of learning that take place outside of a traditional learning environment and can be alternatives for learners who are not able to study in a traditional environment for various reasons. With advancement in technologies and increased use of smart phone, mobile learning has gained popularity as another form of learning and has enabled learners to learn anywhere and anytime. Ubiquitous learning takes mobile learning to another level by providing contents that are context and location aware. There is therefore the need to provide mobile devices with the right learning contents for the right users. The right learning contents should be adaptive to the learner’s location, as well as learning style and device etc. To be able to implement the learning, learning management systems play the important role in creating, managing, and delivering the learning contents. In this paper, a location-based Learning Management System for adaptive and personalized mobile learning is presented. The systems makes use of 5R Adaptation Framework for Location based Mobile learning, the location-based dynamic grouping algorithm, and concepts of the IMS Learning Design model to produce a location-based adaptive mobile learning setting

Classification of learning styles using behavioral features and twin support vector machine

Background and Objective:Internet and computer access have created opportunities for e-learning. Easier access to resources and freedom of action for users is one of the benefits of e-learning. However, e-learning is not as attractive and dynamic as traditional or face-to-face instruction, and in these systems the user's condition, such as learning rate and motivation, is not taken into account. Therefore, the developers of e-learning systems can help to solve the problems mentioned in these systems by considering the learning style and design of interactive user relationships. Automated identification of learning style not only increases the attractiveness of e-learning, but also increases the efficiency and motivation of learners in e-learning environments. Research shows that people differ in decision making, problem solving, and learning. Learning style makes people understand a story differently. For example, people with good visual memory prefer to present topics visually rather than orally. Applying a proper teaching method improves the learner's performance in the learning environment. Lack of attention to students' learning style reduces their motivation and interest in studying and engagement in educational courses. Students’ success is one of the prominent goals in the learning environments. In order to achieve this goal, paying attention to students’ learning style is essential. Being aware of students’ learning style helps to design an appropriate education method which improves student’s performance in the learning environments. In this paper, the aim is to create a model for automatic prediction of learning styles. Methods: Therefore, two real datasets collected from an e-learning environment which consists of 202 electrical and computer engineering students. Behavioral features were extracted from users’ interaction with e-learning system and then learning styles were classified using twin support vector machine. Twin support vector machine is an extension of SVM which aims at generating two non-parallel hyperplanes. This classifier is not sensitive to imbalanced datasets and its training speed is fast. Findings: In this study, increasing the attractiveness of e-learning is emphasized and the issue of automatic recognition of students' learning style has been investigated by MBTI model. Two data sets from the interaction of 202 electrical and computer engineering students with the Moodle e-learning system have been collected. The collected data set is very unbalanced, which has a negative effect on the accuracy of the categories. With this in mind, the twin support vector machine uses the least squares as a binder. The distinctive feature of this category is the low sensitivity to data balance and very high speed. The results show that the proposed method, despite the inconsistency of the data, has performed very well in the classification of students' learning style and accurately recognizes 95% of learning styles.Conclusion: Due to the excellent performance of the proposed method, a new component can be added to e-learning systems such as Moodle by identifying the learning style, content and appropriate teaching method for the learner. Future research could also gather more data from an e-learning environment and categorize learning styles with cognitive characteristics from the learner.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Development of a Myers-Briggs Type Indicator Based Personalised E-Learning System

The major challenge of the traditional learning system is space-time restriction and it is teacher-centred. The emergence of Information Technology gave rise for e-learning systems which are characterized with the components of teacher-centred and one-size-fits-all strategy. Subsequently, the concept of personalisation with learning technology was introduced that provides adaptation of learning contents to learning requirements of the learners. Hence, this research paper develops a personalised e-learning system that matches teaching strategy with learners’ learning style using Myers-Briggs Type Indicator (MBTI).  The emphasis is laid on adaptive teaching strategy and revising the teaching strategy for the purpose of increasing learners’ learning performance. The mathematical model is developed for profiling learners to determine their learning style based on the MBTI questionnaire and Dynamic Bayesian Network is applied to revise the teaching strategy. The system is implemented using PHP and Wamp server and the database is designed using Structured Query Language (SQL). The developed system is tested using Undergraduate students studying Information Technology at Federal University of Technology, Minna. The percentage analysis of the students’ scores shows that 78% of students passed and the remaining 22% passed when the strategy was revised. The performance evaluation of the system is carried out and from the analysis it can be concluded that the Myers-Briggs Type Indicator Based Personalised E-learning System developed is appealing to students and the performance of students improved significantly

Human-computer interaction in intelligent tutoring systems

Due to the rapid evolution of society, citizens are constantly being pressured to obtain new skills through training. The need for qualified people has grown exponentially, which means that the resources for education/training are significantly more limited, so it's necessary to create systems that can solved this problem. The implementation of Intelligent Tutoring Systems (ITS) can be one solution. Besides, ITS aims to enable users to acquire knowledge and develop skills in a specific field. To achieve this goal, the ITS should learn how to react to the actions and needs of the users, and this should be achieved in a non-intrusive and transparent way. In order to provide personalized and adapted system, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behaviour becomes an essential aspect for the successful implementation of an ITS. In this article, we present the student model of an ITS, in order to monitor the user's biometric behaviour and their learning style during e-learning activities. In addition, a machine learning categorization model is presented that oversees student activity during the session. Additionally, this article highlights the main biometric behavioural variations for each activity, making these attributes enable the development of machine learning classifiers to predict users' learning preferences. These results can be instrumental in improving ITS systems in e-learning environments and predict user behaviour based on their interaction with computers or other devices.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Developing a Method of Recommending E-Learning Courses Based on Students’ Learning Preferences

In designing e-learning, it is desirable that individual learner’s learning style is considered. This study proposes a way to present the information about the expected adaptability of the course, in which a student wishes to enroll, based on the student’s responses to the learning preference questionnaire administered at the beginning of the course. As the result of applying the real data to the model derived, it was confirmed that it would be possible to estimate the course adaptability before taking the course and to provide the information for the student to improve his/her course adaptability based on the student’s responses to the learning preference questionnaire.15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011), September 12-14, 2011, Kaiserslautern, German

AdPisika: an adaptive e-learning system utilizing k-means clustering, decision tree, and bayesian network based on felder-silverman model to enhance physics academic performance

Amid the shift to online learning during the COVID-19 outbreak, the academic performance of students has become a concern. To address this, Adaptive Learning Systems (ALS) have emerged, these help in assessing students and delivering personalized content. This study develops an ALS incorporating K-means Clustering, Decision Tree, and Bayesian Network techniques, based on the Felder-Silverman Learning Style Model (FSLSM). The aim is to optimize learning materials based on students' current Knowledge Level (KL) and their Learning Style (LS). The students who utilized the proposed system showed substantial improvements in their performance across the Electromagnetic Spectrum, Light, Electricity, and Magnetism modules, with increases of 28.8%, 41.4%, 31.9%, and 32.9%, respectively. These findings provide strong evidence that the adaptive e-learning system had a significant positive impact on post-test scores compared to pre-test scores, surpassing the outcomes achieved with the traditional learning approach. With a silhouette score of 0.7 for K-Means clustering, an accuracy of 87.5% for Decision Tree, and a 95.1% acceptance value for the distribution of learning objects using the Bayesian Network, the proposed adaptive system demonstrated successful implementation of these machine learning algorithms. Furthermore, the proposed system received "excellent" ratings for functional stability, performance efficiency, compatibility, and reliability, with mean values of 4.49, 4.43, 4.43, 4.8, and 4.47 respectively

Redesigning Course Management Systems through Applying Andragogy and Pedagogy Learning Theory

Nowadays, students in hi,gher educational institutions need to compete with each other to become the best among the best. In order to be the best, they need to become an excellent student in their study. The objectives of this project are to study higher education students' information seeking behavior in virtual learning environment, conduct a research to understand the principles of pedagogy and andragogy learning style and then all the information will be combined and analyzed together to redesign the Course Management System throu~ applying the pedagogy and andragogy learning theory to make it more suitable and effective learning environment for adult learners. The design and learning principles of CMS assume pedagogical learning for all learners, including higher education students. Pedagogical learning defines instructor controlled environment Sometimes, there are parts or situations that require andragogical concepts to make the CMS more effective. A study on technical feasibility, schedule feasibility, economic feasibility and operational feasibility has been done to determine the scope of study. This project will be using Moodie, a free and open-source e-learning software platform and the development of the system is expected to be completed within two (2) semesters. For the methodology of this project, Instructional Design (ID) will be used. It is a system approach to designing systems that meet the learners' needs. The ADDIE model for instructional design (ISD) consists of five-phase generic model which are analysis, design, development, implementation and evaluation. Each step has an outcome that feeds the next step in the sequence and one of the advantages of using ADDIE model is to ensure the effectiveness of the pro,gram using processes with specific, measurable outcomes. After a few studies have been done, the principles of andragogy and pedagogy learning style have been carried out Pedagogy is an art of teaching children while andragogy is the style of adult learning. There are various CMS that offer various features that will support pedagogy and andragogy style. So, some of the features will be combined together to redesign the Course Management System (CMS) that support both pedagogy and andragogy learnin,g theories