The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

A systematic review: machine learning based recommendation systems for e-learning

Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners’ sequences according to a specific learning style model using clustering algorithms. To perform our approach we use Felder-Silverman Model as LSM and Fuzzy C-Means as a clustering algorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results.

A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

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.

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Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

With the technological revolution of Internet and the information overload, adaptive E-learning has become the promising solution for educational institutions since it enhances students' learning process according to many factors such as their learning styles. Learning styles are a criteria of great import in E-learning environment because they can help the system to effectively personalize students' learning process. Generally, the traditional way of detecting students' learning style is based on asking students to fill out a questionnaire. However, using this static technique presents many problems. Some of these problems include the lack of self-awareness of students of their learning preferences. In addition, almost all students are bored when they are asked to fill out a questionnaire. Thus, in this work, we present an automatic approach for detecting students' learning style based on web usage mining. It consists in classifying students' log files according to a specific learning style model (Felder and Silverman model) using clustering algorithms (K-means algorithm). In order to test the efficiency of our work, we use a real-world dataset gathered from an E-learning system. Experimental results show that our approach provide promising results.

Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach

The aim of this research is to reveal relations between Kolb’s learning styles and mobile learning readiness of pre-service teachers in depth in regard to different variables and identify their mobile learning perspectives. The study group consisted of 352 students enrolled in undergraduate programs in education faculties of different universities in Turkey. The convergent parallel design was used as a mixed method strategy. The survey model, as a quantitative component, was used to describe the present situation and embedded interviews, as a qualitative component, were carried out to deeply reveal pre-service teachers’ perspectives on mobile learning depending on their learning styles. The “Learning Styles Inventory - Version III” as well as the “Mobile Learning Readiness Scale” were administered to participants. ANOVA, Tukey-HSD test and Structural Equation Modelling were used to analyze the quantitative data. The qualitative data were analyzed by the content analysis method. Results suggest that 126 (36%) of the pre-service participating in the study were with the assimilating learning style, 92 (26.29%) participants were with the diverging learning style, 73 (20.85%) were with the converging learning style and 59 (16.85%) were with the accommodating learning style. Furthermore, it was observed that there is a statistically significant relationship between the learning styles of the pre-service teachers and their m-learning readiness. In addition, it was observed that while optimism, self-directed learning and self-efficacy have a strong effect on m-learning; mother education, monthly income, gender, internet use frequency have a moderate effect on m-learning within different learning styles. Qualitative data were also in line with the results of quantitative data. Findings were discussed in light of relevant literature.

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Exploring relationships between Kolb’s learning styles and mobile learning readiness of pre-service teachers: A mixed study

due to growing popularity of E-Learning, personalization has emerged as important need. Differences of learners' abilities and their learning styles have affected the learning outcomes significantly. Meanwhile, with the development of E-Learning technologies, learners can be provided more effective learning environment to optimize their performance. The purpose of this study is to determine the impact of learning styles on learner’s performance in e-learning environment, and use this learning style data to make recommendations for learners, instructors, and contents of online courses. Data analysis in this research represented by user performance gathered from an E-learning platform (Blackboard), where this user performance data is represented by actions performed by platform's users. A 10-fold cross validation was used to create and test the model, and the data was analyzed by the WEKA software. Classification accuracy, MAE, and the ROC area have been observed. The results show that the accuracy of classification by means of NBTree technique had the highest correct value at 69.697% and it could be applied to develop Felder Silverman's learning style while taking into consideration students’ preference. Moreover, students’ performance increased by more than 12%.

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Malak Sadik

Papers

The Impact of Learning Styles on Learner's Performance in E-Learning Environment