Intelligent E-learning System Research Articles

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Intelligent e-learning design for art courses based on adaptive learning algorithms and artificial intelligence

With the development of digital technology and the popularity of the Internet, e-learning has become an increasingly popular learning method. However, current e-learning systems often lack personalization and intelligence, and cannot meet the different learning needs of students. This paper designed an intelligent e-learning system using adaptive learning algorithm and artificial intelligence technology. The system aims to provide a personalized learning experience to help students efficiently master the knowledge of art courses. We introduce in detail the design and implementation of the intelligent e-learning system, which uses the adaptive learning algorithm to continuously adjust the learning content and learning methods by analyzing the students’ learning behaviors and characteristics. The system provide intelligent tutoring and evaluation functions to help students with autonomous learning and feedback. By comparing the experiment with the traditional e-learning system, we find that the intelligent e-learning system has significant advantages in improving the learning effect and learning motivation. After using the intelligent e-learning system, students’ knowledge mastery and learning interest have been significantly improved.

Towards intelligent E-learning systems.

The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning.

Adaptive System to Improve the Acquisition of Educational Knowledge for Learners

The digital age in which we live today is witnessing technological development and an information revolution in various fields. This technological development has been imposed on education and it has become important to recognise adaptive e-learning systems as one of the most interesting research areas in online distance education. This area of research allows developers to build a distinct learning environment that meets the needs of each learner separately, in order to adapt learning to their needs and characteristics. This paper aims to give an overview of state-of-the-art adaptive and intelligent e-learning systems by describing their features and architecture, as well as the actual implementations. the authors also highlight some prospects for future work by reviewing, discussing and analysing existing systems. In addition, the authors propose an overall architecture with the system components that will be used to run our mobile application.

Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems

Motivation to engage in learning is essential for learning performance. Learners’ motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners’ motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.

Towards Intelligent E-Learning Systems: A Hybrid Model for Predicatingthe Learning Continuity in Iraqi Higher Education

E-Learning system gains a great attention in the past years; with advance of the internet and the information exchange techniques the importance to merge the traditional learning means with the internet-based learning methods became a must especially in Iraq, the Iraqi higher education is now coping with the new information and communication technologies and adopting a modern methods for upgrading their education and learning ways. There are great efforts to blend E-Learning systems with the educational process, in order to fulfill this purposes the proposed research is advancing E-Learning systems by suggesting a hybrid method that combines two Artificial Intelligence Techniques (AI) inside the design and the development of an intelligent E-Learning system for computer science department at university of technology. The utilization of Artificial Neural Networks algorithm (ANNs) especially Recurrent Neural Networks (RNN) is a way of implementing deep learning technique to predict the students' final out comes in virtual class room based on their grades and their learning behaviors. RNN is optimized by utilizing ADAM optimizer to lift the accuracy of the proposed algorithm, the dataset are gathered and processed to suite the education purposes and was divided into80% for training the model and 20% for testing the model, the results of the hybrid model are compared with other machine learning methods like Multi-Layer Perceptron (MLP), decision tree, naïve Bayesian, and random forest using WEKA environment, the results of the proposed model showed a promising accuracy when compared with the mentioned machine learning algorithms.

Student model initialization using domain knowledge ontology representative subset

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is described. The approach defines which concepts from domain knowledge should be included in the initial test so the system can make conclusions about what students truly know about domain knowledge. This representative subset of domain knowledge is defined using non-semantic mathematical approach based on graph theory. The initial test, created over a domain knowledge representative subset, guarantees encompassing all concepts that are relevant to domain knowledge. A two-level case study is conducted on what would be the representative subset of one selected domain knowledge. It compares semantically selected domain knowledge representative subsets (semantical analysis was done by domain area experts) to a non-semantical, mathematically selected domain knowledge representative subset. The results of the case study show that problems of inequality of semantically selected domain knowledge representative subsets are easily overcome using the presented approach.

Classification and analysis of users review using different classification techniques in intelligent e-learning system

The internet comprised of a large number of data in the form of text, images, stickers, etc. which is also called as reviews or feedbacks created by users to share their expressions or knowledge. All those data may be in a different kind like positive, negative or neutral, and sometimes it may be in a single word or a single sentence or in document form. There are a few techniques, which are measured to provide better classifier like classification-support vector machine (SVM), Naive Bayes (NB) and KNN. Using 'word tokenizer' in the techniques like (SVM, Naive, KNN, J48, DT) it is compared with different results (accuracy, sensitivity, specificity, ppv and npv). It has been observed that after using various tokenizer in Weka tool (alphabetic tokenizer) has provided better results in measure, i.e., SVM (86.39%) comparing to techniques and specificity (83.77%) in average comparing to other measures.

Classification and analysis of users review using different classification techniques in intelligent e-learning system

Classification and analysis of users review using different classification techniques in intelligent e-learning system

ВОПРОСЫ АДАПТИВНОСТИ В СИСТЕМАХ ДИСТАНЦИОННОГО ОБУЧЕНИЯ

This paper considers the problems of adaptivity realization in intelligent e-learning tutoring systems, based on adaptive hypermedia technologies and individual learning styles models.

An intelligent adaptive fuzzy-based inference system for computer-assisted language learning

Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners’ needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners’ misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students’ needs and preferences.

Intelligent e-learning system based on fuzzy logic

In this technological world demanding latest updations in the domain knowledge, it is no surprise that e-learning has become a more viable option to a range of people from beginners to get knowledged and the experts to get updated in a particular domain. Nevertheless, the evolution of e-learning systems is yet to provide full adaptability to the e-learners due to several weaknesses in the systems. Normally, e-learners have varying degrees of progress in their respective learning methodology. Over a period of time, this affects the e-learners performance while providing the same course to all e-learner. Hence, there is a need to create the adaptive e-learning environment to offer the appropriate e-learning contents to all the e-learners. This proposed work puts forth a fuzzy-based novel, intelligent and adaptive e-learning context for a programming language and offers appropriate domain contents to the e-learners which shall update the e-learners better than the previous works. The dependency relation among the concepts in the programming language is provided using fuzzy cognitive map which in turn paves the way for the development of the existing e-learning system. The fuzzy sets and the fuzzy rules represent the e-learners knowledge level and help in providing appropriate recommendations for the previous and subsequent related concepts in a fuzzy cognitive map. Evaluations of the proposed intelligent e-learning system provide promising results in the precise categorization of e-learners and to find their true knowledge.

Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in e-learning systems and a few of them have seriously affected the learning goals of users. Thus, an intelligent e-learning system have been developed which can recommend professional and targeted courses according to their career goals. First, an enhanced collaborative filtering (CF) approach is proposed considering users' career goals and background information. Then, the relevance between career goals and courses are calculated to alleviate the cold-start problem and recommend specialized courses for users. Finally, a PrefixSpan algorithm is combined with the above methods to generate a personalized learning path step by step. Some experiments are carried out with real users of different professions to test the performance of the hybrid algorithm.

Intelligent web-based E-learning system

This paper is based on Web Application and it is host on the cloud web server. The main theme of that paper is study oriented material are bind to form the single website. It is mainly used for students to refer there department’s major paper oriented topic. This paper severs in the way of a video tutorial, presentation and relevant edition of book. It can be categories into a different part of languages C, C++, JAVA, database, computer networks etc. Whatever area the user wants they can be searched to use that material. There are three categories of user they are an admin, moderator and visitor. Admin having all permission to view the detail of the users. This will help to know what requirement the user needs. The visitor is a general user to visit the website to surf the study content and having some doubt about the subject they permit to post on that site. Moderator is the intermediate between the admin and the visitor, they have to upload the videos and presentation content and have to reply the posted questions also. The web framework was developed with the help of python-Django 1.11.1. It is one of the web frameworks that has inbuilt module and API. The user gets login with the help of Google authentication. In that way they detail can be stored in the database. Sqlite3 version database is used for fast retrieving and storing the data. The front end of this project is using HTML, CSS, and JavaScript. This will provide the good user-interface. The hijacker module is used to improvise the search content.

Emotions recognition in an intelligent elearning environment

ABSTRACTFor the purpose of improving the quality in Elearning process and overcoming the limitations of the current online educational environments, we propose to take into consideration the emotional states of students during Elearning sessions. Our objective is to ensure the ability of emotional intelligence: Emotion Recognition, in an eLearning environment. Thus, we present an architecture of Emotionally Intelligent Elearning System (EIES). Within the development of a computational probabilistic model of emotions, we proposed a Bayesian Network (BN) model to deal with emotions in Elearning environments and handle the uncertain nature of emotion recognition process. In a second phase, we focus on the incorporation of the emotion recognition in the Elearning systems by developing a simulation of EIES based on the BN model, able to predict the students’ affects. Consequently, we reached positive and promising results related to the fact that simulated EIES based on the BN model of emotions predicts correctly the student’s emotion when an event occurs during an Elearning session.

Adaptive feedback in computer-based learning environments: a review

Adaptive support within a learning environment is useful because most learners have different personal characteristics such as prior knowledge, learning progress, and learning preferences. This study reviews various implementation of adaptive feedback, based on the four adaptation characteristics: means, target, goal, and strategy. This review focuses on 20 different implementations of feedback in a computer-based learning environment, ranging from multimedia web-based intelligent tutoring systems, dialog-based intelligent tutoring systems, web-based intelligent e-learning systems, adaptive hypermedia systems, and adaptive learning environment. The main objective of the review is to compare computer-based learning environments according to their implementation of feedback and to identify open research questions in adaptive feedback implementations. The review resulted in categorizing these feedback implementations based on the students’ information used for providing feedback, the aspect of the domain or pedagogical knowledge that is adapted to provide feedback based on the students’ characteristics, the pedagogical reason for providing feedback, and the steps taken to provide feedback with or without students’ participation. Other information such as the common adaptive feedback means, goals, and implementation techniques are identified. This review reveals a distinct relationship between the characteristics of feedback, features of adaptive feedback, and computer-based learning models. Other information such as the common adaptive feedback means, goals, implementation techniques, and open research questions are identified.

An Enhanced Learning Style Index: Implementation and Integration into an Intelligent and Adaptive e-Learning System

Advances and accessibility of Internet services around the world have transformed the traditional classroom learning into web-based e-learning systems. In recent years, designing adaptive e-learning systems has become one of striking topic of discussions in the literature. Additionally, integrating such systems with intelligent and adaptive systems that can measure the learning preferences of the user can enable learners to obtain the most suitable learning objects that might be matched with their learning styles. Moreover, even in the classroom teaching, knowing the learning styles of students can also help teachers to adopt appropriate learning materials for efficient learning. This paper is concerned with the study, implementation, and application of a web-based learning style index. The paper also described a case study on the integration of the learning style index into an adaptive and intelligent e-learning system.

Case-Based Reasoning and Learning Systems

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.

Case-Based Reasoning and Learning Systems

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.

Recommending learning material in Intelligent Tutoring Systems

<p>Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.</p>