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

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

Explosive growth of e-learning in the recent years has faced difficulty of locating appropriate learning resources to match the students learning styles. Recommender system is a promising technology in e-learning environments to present personalised offers and convey appropriate learning objects that match student inclinations. This paper, proposes a novel and effective recommender algorithm that recommends personalised learning objects based on the student learning styles. Various similarity metrics are considered in an experimental study to investigate the best similarity metrics to use in a recommender system for learning objects. The approach is based on the Felder and Silverman learning style model which is used to represent both the student learning styles and the learning object profiles. It was found that the K-means clustering algorithm, the cosine similarity metrics and the Pearson correlation coefficient are effective tools for implementing learning object recommender systems. The accuracy of the recommendations are measured using traditional evaluation metrics, namely the Mean Absolute Error and the Root Mean Squared Error

An Adaptive Learning Ontological Framework Based on Learning Styles and Teaching Strategies

Ontology are increasingly being used in a variety of applications, and particularly in adaptive e-learning. They have the potential to enable developers to create adaptive course content for specified domains. E-learning applications are thus able to use technology and educational content in order to generate content that matches the student's capabilities and knowledge. This personalises learning, rather than assuming that "one-size-fits-all" and providing all learners with the same content, which is what the majority of e-learning systems do. This study introduces a new approach that takes into account the fact that each learner has an individual learning style and needs. The approach enables to adapt the course content, teaching strategy and learning objects so that they correspond to each student’s learning styles. This is achieved with the use of artificial intelligent in the form of an ontology and rule-based reasoning. The proposed system takes some of the key design aspects such as extensibility, reusability, and maintainability into consideration in order to enhance performance of adaptive course content recommendation

ULEARN: Personalized course Learning Objects based on Hybrid Recommendation approach

Adaptive e-learning recommender system is observed one of the exciting research discipline in the education and teaching throughout the past few decades, since, the learning style is specific for each student In reality from the knowledge of his/ her learning style; matching teaching strategy with the most appropriate learning object is present to better return on learner academic level. This work focuses on the design of a personalized e-learning environment based on hybrid recommender system based on collaborative filtering and item content filtering as well as architecture of ULEARN system. ULEARN recommended adaptive teaching strategy by choosing and sequencing learning objects fitting with the learners’ learning styles. The proposed system can be used to rearrange learning object priority that matches student adaptive profile and teaching strategy in order to improve the quality of learning

A Novel Adaptive Learning Management System Using Ontology

The success of web technologies has prompted a developing consideration on e-learning activities. Notwithstanding, most current e-Learning systems give static web-based learning with the goal that learners get to the same learning contents through the web, regardless of individual learners profiles. These learners may have altogether different learning foundations, information levels, learning styles, and capacities. The 'one size fit all' in an e-Learning frameworks is unmistakably a commonplace issue. To defeat this impediment and build powerful learning, versatile and customized learning is as of now a dynamic examination range. This paper propose a novel approach for designing and implementing adaptive learning management system based on ontology and semantic web technologies by offering a tailored model which represents the different activities that should be completed by learner. It offers a framework that is based on both learning styles and ontology to address the impact of student behavior

Personalized Students' Profile Based on Ontology and Rule-based Reasoning

Nowadays, most of the existing e-learning architecture provides the same content to all learners due to "one size fits for all" concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized environment that involves collecting a range of information about each learner. Questionnaires are one way of gathering information on learning style, but there are some problems with their usage, such as reluctance to answer questions as well as guesses the answer being time consuming. Ontology-based semantic retrieval is a hotspot of current research, because ontologies play a paramount part in the development of knowledge. In this paper, a novel way to build an adaptive student pro le by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory is proposed

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method