A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms
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paper_title: Rethinking Pedagogy for a Digital Age : Designing for 21st Century Learning

paper_content:

Table of Contents An introduction to rethinking pedagogy by Helen Beetham and Rhona Sharpe Part One: Principles and practices of designing for learning Chapter 1 Technology enhanced learning: the role of theory by Terry Mayes and Sara de Freitas Chapter 2 Designing for active learning in technology-rich contexts by Helen Beetham Chapter 3 The analysis of complex learning environments by Peter Goodyear and Lucila Carvalho Chapter 4 The challenge of teachers' design practice by Liz Masterman Chapter 5 Tools and resources to guide practice by By Grainne Conole Chapter 6 Describing ICT-based learning designs that promote quality learning outcomes by Ron Oliver, Barry Harper, Sandra Wills, Shirley Agostinho and John Hedberg Chapter 7 Learning designs as stimulus and support for teachers' design practices by Shirley Agostinho, Sue Bennett, Lori Lockyer, Jennifer Jones and Barry Harper Chapter 8 Representing practitioner experiences through learning designs and patterns by Patrick McAndrew and Peter Goodyear Chapter 9 The influence of open resources on design practice by Chris Pegler Part Two: Contexts for design Chapter 10 Designing for learning in course teams by Rhona Sharpe and Martin Oliver Chapter 11 The art of design by Derek Harding and Bruce Ingraham Chapter 12 Activity designs for professional learning by Rachel Ellaway Chapter 13 Designing for practice: A view from social science by Chris Jones Chapter 14 Student as producer is hacking the university by Joss Winn and Dean Lockwood Chapter 15 The LAMS community: Building communities of designers by James Dalziel Chapter 16 Design principles for mobile learning by Agnes Kukulska-Hulme and John Traxler Chapter 17 Designing for learning in an uncertain future by Helen Beetham
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paper_title: A Shortest Learning Path Selection Algorithm in E-learning

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Generally speaking, in the e-learning systems, a course is modeled as a graph, where each node represents a knowledge node (KU) and two nodes are connected to form a semantic network. The desired knowledge is provided by the student as a direct request or from search results, mapping the owned knowledge onto the target knowledge. How to select a learning path which costs the least time and effort is critical. In this paper, we described the relationships between different nodes in the graph structure of knowledge units and propose an algorithm to select the shortest learning paths to learn the target knowledge.
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paper_title: e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning

paper_content:

In this thoroughly revised edition of the bestselling e-Learning and the Science of Instruction authors Ruth Colvin Clark and Richard E. Mayer internationally-recognized experts in the field of e-learningoffer essential information and guidelines for selecting, designing, and developing asynchronous and synchronous e-learning courses that build knowledge and skills for workers learning in corporate, government, and academic settings. In addition to updating research in all chapters, two new chapters and a CD with multimedia examples are included.
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paper_title: Staying the Course: Online Education in the United States, 2008.

paper_content:

Using responses from over 2,500 colleges and universities, the study examined the numbers of students involved in online education and the impact of this education for different disciplines and contexts.This report is the sixth in a series of annual reports on a study conducted by the Babson Survey Research Group for the Sloan Consortium. Using responses from over 2,500 colleges and universities, the study sought answers to several questions on online education: * How many students are learning online? * What is the impact of the economy on online enrollments? * Is online learning strategic? * What disciplines are best represented online?
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paper_title: User modeling for adaptive e-learning systems

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Adaptive systems have been a hot topic in various areas like hypermedia systems, e-commerce systems, e-learning environments and information retrieval. In order to provide adaptivity, these systems need to keep track of different types of information about their users. Therefore, user modeling is at the heart of the adaptation process. In this paper, different user modeling techniques will be reviewed with the focus on what needs to be modeled and how it will be modeled, i.e., the demographic information of the users are collected in most of these systems, however, how it will be used in the adaptation process depends on the methodology being followed. The evaluation of different user modeling approaches and examination of some recent adaptive e-learning systems' architectures will also be provided.
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paper_title: Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

paper_content:

Technological progress in recent decades has enabled people to learn in different ways. Universities now have more educational models to choose from, i.e., b-learning and e-learning. Despite the increasing opportunities for students and instructors, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students’ performance. In this paper, we aim to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data. Their origins, goals, differences, similarities, time evolution, and challenges are addressed, as are their relationship with Big Data and MOOCs.
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paper_title: Learning analytics and educational data mining: towards communication and collaboration

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Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
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paper_title: Educational Data Mining: A Review of the State of the Art

paper_content:

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.
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paper_title: Review: Educational data mining: A survey and a data mining-based analysis of recent works

paper_content:

This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.
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paper_title: Customer Relationship Management applied to higher education: developing an e-monitoring system to improve relationships in electronic learning environments

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Customer Relationship Management (CRM) has usually been associated with business contexts. However, it has recently been pointed out that its principles and applications are also very appropriate for non-profit making organisations. In this article, we defend the broadening of the field of application of CRM from the business domain to a wider context of relationships in which the inclusion of non-profit making organisations seems natural. In particular, we focus on analysing the suitability of adopting CRM processes by universities and higher educational institutions dedicated to electronic learning (e-learning). This is an issue that has much potential but has received little attention in research so far. Our work reflects upon this matter and provides a new step towards a CRM solution for managing relationships of specific customers, such as students. Indeed, the main contribution of this article is specifically characterised by the proposal and empirical application of an e-monitoring system that aims to enhance the performance of relationships in e-learning environments.
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paper_title: Data mining in education

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Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.
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paper_title: A data analysis model based on control charts to monitor online learning processes

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This paper discusses the convenience of using data analysis techniques to monitor e-learning processes. It also proposes a model to monitor online students' academic activity and performance. Using data from log files and databases, this model determines which information has to be provided to online instructors and students. These reports allow instructors to: classify students according to their activity and learning outcomes, track their evolution, and identify those who might need immediate assistance. They also provide students with a periodical feedback which makes them aware of how they are performing as compared with the rest of the class.
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paper_title: Using Collaboration Strategies to Support the Monitoring of Online Collaborative Learning Activity

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This paper first discusses the importance of online education and highlights its main benefits and challenges. In this context, on the one hand, we argue the significance of monitoring students’ and groups’ activity in an online learning environment. On the other hand, we analyze the informational needs that should be covered by any monitoring information system. Finally, the paper goes a step further by proposing the use of collaboration strategies as a manner to improve monitoring and learning processes in computer-supported collaborative learning.
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paper_title: Handbook of educational data mining

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Preface, Joseph E. Beck Introduction, Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan Baker Basic Techniques, Surveys, and Tutorials Visualization in Educational Environments, Riccardo Mazza Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments, Judy Sheard A Data Repository for the EDM Community: The PSLC DataShop, Kenneth R. Koedinger, Ryan Baker, Kyle Cunningham, Alida Skogsholm, Brett Leber, and John Stamper Classifiers for EDM, Wilhelmiina Hamalainen and Mikko Vinni Clustering Educational Data, Alfredo Vellido, Felix Castro, and Angela Nebot Association Rule Mining in Learning Management Systems, Enrique Garcia, Cristobal Romero, Sebastian Ventura, Carlos de Castro, and Toon Calders Sequential Pattern Analysis of Learning Logs: Methodology and Applications, Mingming Zhou, Yabo Xu, John C. Nesbit, and Philip H. Winne Process Mining from Educational Data, Nikola Trcka, Mykola Pechenizkiy, and Wil van der Aalst Modeling Hierarchy and Dependence among Task Responses in EDM, Brian W. Junker Case Studies Novel Derivation and Application of Skill Matrices: The q-Matrix Method, Tiffany Barnes EDM to Support Group Work in Software Development Projects, Judy Kay, Irena Koprinska, and Kalina Yacef Multi-Instance Learning versus Single-Instance Learning for Predicting the Student's Performance, Amelia Zafra, Cristobal Romero, and Sebastian Ventura A Response-Time Model for Bottom-Out Hints as Worked Examples, Benjamin Shih, Kenneth R. Koedinger, and Richard Scheines Automatic Recognition of Learner Types in Exploratory Learning Environments, Saleema Amershi and Cristina Conati Modeling Affect by Mining Students' Interactions within Learning Environments, Manolis Mavrikis, Sidney D'Mello, Kaska Porayska-Pomsta, Mihaela Cocea, and Art Graesser Measuring Correlation of Strong Symmetric Association Rules in Educational Data, Agathe Merceron and Kalina Yacef Data Mining for Contextual Educational Recommendation and Evaluation Strategies, Tiffany Y. Tang and Gordon G. McCalla Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles, Daniela Godoy and Analia Amandi Log-Based Assessment of Motivation in Online Learning, Arnon Hershkovitz and Rafi Nachmias Mining Student Discussions for Profiling Participation and Scaffolding Learning, Jihie Kim, Erin Shaw, and Sujith Ravi Analysis of Log Data from a Web-Based Learning Environment: A Case Study, Judy Sheard Bayesian Networks and Linear Regression Models of Students' Goals, Moods, and Emotions, Ivon Arroyo, David G. Cooper, Winslow Burleson, and Beverly P. Woolf Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources, David Masip, Julia Minguillon, and Enric Mor Anticipating Student's Failure as soon as Possible, Claudia Antunes Using Decision Trees for Improving AEH Courses, Javier Bravo, Cesar Vialardi, and Alvaro Ortigosa Validation Issues in EDM: The Case of HTML-Tutor and iHelp, Mihaela Cocea and Stephan Weibelzahl Lessons from Project LISTEN's Session Browser, Jack Mostow, Joseph E. Beck, Andrew Cuneo, Evandro Gouvea, Cecily Heiner, and Octavio Juarez Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks, Zachary A. Pardos, Neil T. Heffernan, Brigham S. Anderson, and Cristina L. Heffernan Mining for Patterns of Incorrect Response in Diagnostic Assessment Data, Tara M. Madhyastha and Earl Hunt Machine-Learning Assessment of Students' Behavior within Interactive Learning Environments, Manolis Mavrikis Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator, Philippe Fournier-Viger, Roger Nkambou, and Engelbert Mephu Nguifo Using Markov Decision Processes for Automatic Hint Generation, Tiffany Barnes, John Stamper, and Marvin Croy Data Mining Learning Objects, Manuel E. Prieto, Alfredo Zapata, and Victor H. Menendez An Adaptive Bayesian Student Model for Discovering the Student's Learning Style and Preferences, Cristina Carmona, Gladys Castillo, and Eva Millan Index
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paper_title: Drawbacks and solutions of applying association rule mining in learning management systems

paper_content:

In this paper, we survey the application of association rule mining in e-learning systems, and especially, learning management systems. We describe the specific knowledge discovery process, its mains drawbacks and some possible solutions to resolve them.
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paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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paper_title: Fuzzy set approach to the assessment of student-centered learning

paper_content:

Assessment of student learning is an important task in a teaching and learning process. It has a strong influence on students' approaches to learning and their outcomes. Development in tertiary education has shifted its emphasis from teacher-centered learning to student-centered learning. In a student-centered learning environment, criterion-referenced assessment techniques are often used in current education research and practice. However, it sometimes happens that the assessment criteria and their corresponding weights are solely determined by the lecturers in charge. This may reduce the interest of students' participation and lower the quality of their learning. This paper presents an integrated fuzzy set approach to assess the outcomes of student-centered learning. It uses fuzzy set principles to represent the imprecise concepts for subjective judgment and applies a fuzzy set method to determine the assessment criteria and their corresponding weights. Based on the commonly agreed assessment criteria, students' learning outcomes are evaluated on a fuzzy grade scale. The proposed fuzzy set approach incorporates students' opinions into assessment and allows them to have a better understanding on the assessment criteria. It aims at encouraging students to participate in the whole learning process and providing an open and fair environment for assessment.
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paper_title: Propose of fuzzy logic-based students' learning assessment

paper_content:

This paper proposes the students' learning assessment by using fuzzy logic. The framework of practical learning system for computer discipline is also presented to explain a conceptual design of an intelligent tutorial system. The proposed framework composes of six components including interface module, domain knowledge, inference engine, student module, mentor module and pedagogical module. The inference engine performed students' group classification form on-line pre-test examination before starting the practical worksheet application. Two input parameters consisting of percentage of score and time were established as inputs for membership functions of fuzzy logic system. The twenty-five fuzzy rules were created for the proposed method by experts. The defuzzification of output membership functions, including good, fair and improve were performed by using the centroid method. In this paper, 26 students were tested in order to compare the students' learning performance, assessed by fuzzy logic and t-score method. The results revealed that the proposed method was a flexible process to classify students' learning group based on the objectives of subject and the real time performance comparing with t-score method.
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paper_title: 1 User Models for Adaptive Hypermedia and Adaptive Educational Systems

paper_content:

One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling.
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paper_title: Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

paper_content:

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions.
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paper_title: Authoring of probabilistic sequencing in adaptive hypermedia with bayesian networks

paper_content:

One of the difficulties that self-directed learners face on their learning pro- cess is choosing the right learning resources. One of the goals of adaptive educational systems is helping students in finding the best set of learning resources for them. Adap- tive systems try to infer the students' characteristics and store them in a user model whose information is used to drive the adaptation. However, the information that can be acquired is always limited and partial. In this paper, the use of Bayesian networks is proposed as a possible solution to adapt the sequence of activities to students. There are two research questions that are answered in this paper: whether Bayesian networks can be used to adaptively sequence learning material, and whether such an approach permits the reuse of learning units created for other systems. A positive answer to both question is complemented with a case study that illustrates the details of the process.
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paper_title: Intelligent student profiling with fuzzy models

paper_content:

Traditional Web-based educational systems still have several shortcomings when comparing with real-life classroom teaching, such as lack of contextual and adaptive support, lack of flexible support of the presentation and feedback, lack of the collaborative support between students and systems. Based on educational theory, personalization increases learning motivation, which can increase the learning effectiveness. A fuzzy epistemic logic has been built to present the student's knowledge state, while the course content is modeled by the concept of context. By applying such fuzzy epistemic logic, the content model, the student model, and the learning plan have been defined formally. A multi-agent based student profiling system has been presented. Our profiling system stores the learning activities and interaction history of each individual student into the student profile database. Such profiling data is abstracted into a student model. Based on the student model and the content model, dynamic learning plans for individual students are made. Students get personalized learning materials, personalized quizzes, and personalized advice. In order to understand the students' perception of our prototype system and to evaluate the students' learning effectiveness, a field survey has been conducted. The results from the survey indicate that our prototype system makes great improvement on personalization of learning and achieves learning effectiveness.
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paper_title: Andes : A coached problem solving environment for physics

paper_content:

Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.
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paper_title: Adaptive Course Sequencing for Personalization of Learning Path Using Neural Network

paper_content:

Advancements in technology have led to a paradigm shift from traditional to personalized learning methods with varied implementation strategies. Presenting an optimal personalized learning path in an educational hypermedia system is one of the strategies that is important in order to increase the effectiveness of a learning session for each student. However, this task requires much effort and cost particularly in defining rules for the adaptation of learning materials. This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system. The ability of soft computing technique in handling uncertainty and incompleteness of a problem is exploited in the study. In this paper we present recent work concerning concept-based classification of learning object using artificial neural network (ANN). Self Organizing Map (SOM) and Back Propagation (BP) algorithm were employed to discover the connection between the domain concepts contained in the learning object and the learner’s learning need. The experiment result shows that this approach is assuring in determining a suitable learning object for a particular student in an adaptive and dynamic learning environment.
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paper_title: Designing Intelligent Tutoring Systems: A Bayesian Approach

paper_content:

This paper proposes a model and an architecture for designing intelligent tutoring system using Bayesian Networks. The design model of an intelligent tutoring system is directed towards the separation between the domain knowledge and the tutor shell. The architecture is composed by a user model, a knowledge base, an adaptation module, a pedagogical module and a presentation module. Bayesian Networks are used to assess user’s state of knowledge and preferences, in order to suggest pedagogical options and recommend future steps in the tutor. The proposed architecture is implemented in the Internet, enabling its use as an e-learning tool. An example of an intelligent tutoring system is shown for illustration purposes.
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paper_title: Fuzzy set approach to the assessment of student-centered learning

paper_content:

Assessment of student learning is an important task in a teaching and learning process. It has a strong influence on students' approaches to learning and their outcomes. Development in tertiary education has shifted its emphasis from teacher-centered learning to student-centered learning. In a student-centered learning environment, criterion-referenced assessment techniques are often used in current education research and practice. However, it sometimes happens that the assessment criteria and their corresponding weights are solely determined by the lecturers in charge. This may reduce the interest of students' participation and lower the quality of their learning. This paper presents an integrated fuzzy set approach to assess the outcomes of student-centered learning. It uses fuzzy set principles to represent the imprecise concepts for subjective judgment and applies a fuzzy set method to determine the assessment criteria and their corresponding weights. Based on the commonly agreed assessment criteria, students' learning outcomes are evaluated on a fuzzy grade scale. The proposed fuzzy set approach incorporates students' opinions into assessment and allows them to have a better understanding on the assessment criteria. It aims at encouraging students to participate in the whole learning process and providing an open and fair environment for assessment.
---
paper_title: Propose of fuzzy logic-based students' learning assessment

paper_content:

This paper proposes the students' learning assessment by using fuzzy logic. The framework of practical learning system for computer discipline is also presented to explain a conceptual design of an intelligent tutorial system. The proposed framework composes of six components including interface module, domain knowledge, inference engine, student module, mentor module and pedagogical module. The inference engine performed students' group classification form on-line pre-test examination before starting the practical worksheet application. Two input parameters consisting of percentage of score and time were established as inputs for membership functions of fuzzy logic system. The twenty-five fuzzy rules were created for the proposed method by experts. The defuzzification of output membership functions, including good, fair and improve were performed by using the centroid method. In this paper, 26 students were tested in order to compare the students' learning performance, assessed by fuzzy logic and t-score method. The results revealed that the proposed method was a flexible process to classify students' learning group based on the objectives of subject and the real time performance comparing with t-score method.
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paper_title: Introduction to Type-2 Fuzzy Logic Control

paper_content:

We describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending the use of fuzzy logic to a higher order, which is called type- 2 fuzzy logic [13]. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers in solving complex control problems.
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paper_title: Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

paper_content:

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions.
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paper_title: Student modeling based on fuzzy inference mechanisms

paper_content:

The paper presents a competence-based instructional design system and a way to provide a personalization of navigation in the course content. The navigation aid tool builds on the competence graph and the student model, which includes the elements of uncertainty in the assessment of students. An individualized navigation graph is constructed for each student, suggesting the competences the student is more prepared to study. We use fuzzy set theory for dealing with uncertainty. The marks of the assessment tests are transformed into linguistic terms and used for assigning values to linguistic variables. For each competence, the level of difficulty and the level of knowing its prerequisites are calculated based on the assessment marks. Using these linguistic variables and approximate reasoning (fuzzy IF-THEN rules), a crisp category is assigned to each competence regarding its level of recommendation.
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paper_title: Intelligent student profiling with fuzzy models

paper_content:

Traditional Web-based educational systems still have several shortcomings when comparing with real-life classroom teaching, such as lack of contextual and adaptive support, lack of flexible support of the presentation and feedback, lack of the collaborative support between students and systems. Based on educational theory, personalization increases learning motivation, which can increase the learning effectiveness. A fuzzy epistemic logic has been built to present the student's knowledge state, while the course content is modeled by the concept of context. By applying such fuzzy epistemic logic, the content model, the student model, and the learning plan have been defined formally. A multi-agent based student profiling system has been presented. Our profiling system stores the learning activities and interaction history of each individual student into the student profile database. Such profiling data is abstracted into a student model. Based on the student model and the content model, dynamic learning plans for individual students are made. Students get personalized learning materials, personalized quizzes, and personalized advice. In order to understand the students' perception of our prototype system and to evaluate the students' learning effectiveness, a field survey has been conducted. The results from the survey indicate that our prototype system makes great improvement on personalization of learning and achieves learning effectiveness.
---
paper_title: Fuzzy User Modeling for Adaptation in Educational Hypermedia

paper_content:

Education is a dominating application area for adap- tive hypermedia. Web-based adaptive educational systems incor- porate complex intelligent tutoring techniques, which enable the system to recognize an individual user and their needs, and con- sequently adapt the instructional sequence. The personalization is done through the user model, which collects information about the user. Since the description of user knowledge and features also in- volves imprecision and vagueness, a user model has to be designed that is able to deal with this uncertainty. This paper presents a way of describing the uncertainty of user knowledge, which is used for user knowledge modeling in an adaptive educational system. The system builds on the concept domain model. A fuzzy user model is proposed to deal with vagueness in the user's knowledge descrip- tion. The model uses fuzzy sets for knowledge representation and linguistic rules for model updating. The data from the fuzzy user model form the basis for the system adaptation, which implements various navigation support techniques. The evaluation of the pre- sented educational system has shown that the system and its adap- tation techniques provide a valuable, easy-to-use tool, which pos- itively affects user knowledge acquisition and, therefore, leads to better learning results.
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paper_title: Inducing Fuzzy Models for Student Classification

paper_content:

We report an approach for implementing predictive fuzzy systems that manage capturing both the imprecision of the empirically induced classifications and the imprecision of the intuitive linguistic expressions via the extensive use of fuzzy sets. From end-users' point of view, the approach enables encapsulating the technical details of the underlying information system in terms of an intuitive linguistic interface. We describe a novel technical syntax of fuzzy descriptions and expressions, and outline the related systems of fuzzy linguistic queries and rules. To illustrate the method, we describe it in terms of a concrete educational user modelling application. We report experiments with two data sets, describing the records of the students attending to a university mathematics course in 2003 and 2004. In brief, we aim identifying the failing students of the year 2004, and develop a procedure for empirically inducing and assigning each student a fuzzy property "poor", which helps capturing the students needing extra assistance. In the educational context, the approach enables the construction of applications exploiting simple and intuitive student models, that to certain extent are self-evident.
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paper_title: An Interval Type-2 Fuzzy Logic Based System for Customised Knowledge Delivery within Pervasive E-Learning Platforms

paper_content:

E-learning involves the computer and network-enabled transfer of skills and knowledge. The internet has become a central core to the educative environment experienced by learners, hence facilitating learning at any location and at any time thus creating pervasive learning environments. There is a growing interest in developing e-Learning platforms which enable the creation of personalized learning environments to suit the students' individual requirements and needs. However, the vast majority of the existing adaptive educational systems do not learn from the users' behaviors to create white box models which could handle the linguistic uncertainties and could be easily read and analyzed by the lay user. This paper presents a type-2 fuzzy logic based system that can learn the users' preferred knowledge delivery based on the students characteristics to generate a personalized learning environment. The type-2 fuzzy model is first created from data acquired from a number of students with different capabilities and needs. The learnt type-2 fuzzy-based model is then used to improve the knowledge delivery to the various students based on their individual characteristics. We will show how the presented system enables customizing the learning environments to improve individualized knowledge delivery to students which can result in enhancing the students' performance. The proposed system is able to continuously respond and adapt to students' needs on a highly individualized basis. Thus, online courses can be structured to deliver customized education to the student based upon various criteria of individual needs and characteristics. The efficiency of the proposed system has been tested through various experiments with the participation of 17 students. These experiments indicate the ability of the proposed type-2 fuzzy logic based system to handle the linguistic uncertainties to produce better performance than the type-1 based fuzzy systems.
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paper_title: Learning achievement evaluation strategy using fuzzy membership function

paper_content:

In this paper, the authors suggest a new learning achievement evaluation strategy in student's learning procedure. They call this fuzzy evaluation. They may assign fuzzy lingual variables to each question pertaining to its importance, complexity and difficulty by using fuzzy membership functions. Then one can evaluate a score depending on the membership degree of uncertainty factors in each question. In addition, they consider the time consuming element for solving a question. They adapt an inverse sigmoid function to consider time consuming elements, fuzzy concentration and dilation function for importance, a sigmoid function for complexity, and fuzzy square method for difficulty.
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paper_title: Rethinking Pedagogy for a Digital Age : Designing for 21st Century Learning

paper_content:

Table of Contents An introduction to rethinking pedagogy by Helen Beetham and Rhona Sharpe Part One: Principles and practices of designing for learning Chapter 1 Technology enhanced learning: the role of theory by Terry Mayes and Sara de Freitas Chapter 2 Designing for active learning in technology-rich contexts by Helen Beetham Chapter 3 The analysis of complex learning environments by Peter Goodyear and Lucila Carvalho Chapter 4 The challenge of teachers' design practice by Liz Masterman Chapter 5 Tools and resources to guide practice by By Grainne Conole Chapter 6 Describing ICT-based learning designs that promote quality learning outcomes by Ron Oliver, Barry Harper, Sandra Wills, Shirley Agostinho and John Hedberg Chapter 7 Learning designs as stimulus and support for teachers' design practices by Shirley Agostinho, Sue Bennett, Lori Lockyer, Jennifer Jones and Barry Harper Chapter 8 Representing practitioner experiences through learning designs and patterns by Patrick McAndrew and Peter Goodyear Chapter 9 The influence of open resources on design practice by Chris Pegler Part Two: Contexts for design Chapter 10 Designing for learning in course teams by Rhona Sharpe and Martin Oliver Chapter 11 The art of design by Derek Harding and Bruce Ingraham Chapter 12 Activity designs for professional learning by Rachel Ellaway Chapter 13 Designing for practice: A view from social science by Chris Jones Chapter 14 Student as producer is hacking the university by Joss Winn and Dean Lockwood Chapter 15 The LAMS community: Building communities of designers by James Dalziel Chapter 16 Design principles for mobile learning by Agnes Kukulska-Hulme and John Traxler Chapter 17 Designing for learning in an uncertain future by Helen Beetham
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paper_title: Adaptive Course Sequencing for Personalization of Learning Path Using Neural Network

paper_content:

Advancements in technology have led to a paradigm shift from traditional to personalized learning methods with varied implementation strategies. Presenting an optimal personalized learning path in an educational hypermedia system is one of the strategies that is important in order to increase the effectiveness of a learning session for each student. However, this task requires much effort and cost particularly in defining rules for the adaptation of learning materials. This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system. The ability of soft computing technique in handling uncertainty and incompleteness of a problem is exploited in the study. In this paper we present recent work concerning concept-based classification of learning object using artificial neural network (ANN). Self Organizing Map (SOM) and Back Propagation (BP) algorithm were employed to discover the connection between the domain concepts contained in the learning object and the learner’s learning need. The experiment result shows that this approach is assuring in determining a suitable learning object for a particular student in an adaptive and dynamic learning environment.
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paper_title: Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

paper_content:

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions.
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paper_title: Andes : A coached problem solving environment for physics

paper_content:

Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.
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paper_title: Designing Intelligent Tutoring Systems: A Bayesian Approach

paper_content:

This paper proposes a model and an architecture for designing intelligent tutoring system using Bayesian Networks. The design model of an intelligent tutoring system is directed towards the separation between the domain knowledge and the tutor shell. The architecture is composed by a user model, a knowledge base, an adaptation module, a pedagogical module and a presentation module. Bayesian Networks are used to assess user’s state of knowledge and preferences, in order to suggest pedagogical options and recommend future steps in the tutor. The proposed architecture is implemented in the Internet, enabling its use as an e-learning tool. An example of an intelligent tutoring system is shown for illustration purposes.
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paper_title: An introduction to hidden Markov models

paper_content:

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
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paper_title: Mining learner–system interaction data: implications for modeling learner behaviors and improving overlay models

paper_content:

A growing body of empirical evidence suggests that the adaptive capabilities of computer-based learning environments can be improved through the use of educational data mining techniques. Log-file trace data provides a wealth of information about learner behaviors that can be captured, monitored, and mined for the purposes of discovering new knowledge and detecting patterns of interest. This study aims to leverage these analytical techniques to mine learner behaviors in relation to both diagnostic reasoning processes and outcomes in BioWorld, a computer-based learning environment that support learners to practice solving medical problems and receive formative feedback. In doing so, hidden Markov models are used to model behavioral indicators of proficiency during problem solving, while an ensemble of text classification algorithms are applied to written case summaries that learners' write as an outcome of solving a case in BioWorld. The application of these algorithms characterize learner behaviors at different phases of problem solving which provides corroborating evidence in support of where revisions can be made to provide design guidelines of the system. We con- clude by discussing the instructional design and pedagogical implications for the
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paper_title: Adaptive E-learning using Genetic Algorithms

paper_content:

Summary In this paper, we describe an adaptive system conceived in order to generate pedagogical paths which are adapted to the learner profile and to the current formation pedagogical objective. We have studied the problem as an "Optimization Problem". Using Genetic Algorithms, the system seeks an optimal path starting from the learner profile to the pedagogic objective passing by intermediate courses. To prepare the courses for adaptation, the application creates a descriptive sheet for resources, in XML format, while its integration in the database. Some experiments are added to this paper.
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Title: A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Section 1: Introduction
Description 1: Introduce the motivation and objectives for enhancing student learning through adaptive educational systems in e-learning, including background information and current trends.

Section 2: Overview of Recent Topics Related to AI techniques for Adaptive Educational Systems
Description 2: Discuss recent developments and topics related to adaptive educational systems, including the importance of modeling individual differences and how AI techniques facilitate adaptive learning.

Section 3: Massive Open Online Courses
Description 3: Explore the emergence and significance of MOOCs, their challenges such as high dropout rates, and how AI techniques can improve the adaptability and personalization of these courses.

Section 4: Educational Data Mining Techniques
Description 4: Detail the machine learning and statistical techniques used in educational data mining (EDM), separating them into predictive and descriptive methods, and their application in adaptive educational systems.

Section 5: Predictive Methods
Description 5: Elaborate on the various predictive methods like classification, regression, and prediction of density, detailing how they are used to forecast educational outcomes and personalize learning experiences.

Section 6: Association Rule Mining
Description 6: Explain the role of association rule mining in uncovering hidden patterns and relationships within educational data to enhance the adaptability of learning systems.

Section 7: Clustering Methods
Description 7: Describe clustering techniques used to group similar data points, aiding in the initial classification and subsequent detailed analysis of educational data.

Section 8: An Overview on Artificial Intelligence Methodologies Employed for Adaptive Educational Systems
Description 8: Provide a comprehensive overview of different AI methodologies like fuzzy logic, decision trees, neural networks, Bayesian networks, hidden Markov models, and genetic algorithms, highlighting their use in adaptive educational systems.

Section 9: Conclusion
Description 9: Summarize the key points discussed in the paper, emphasizing the significance of AI techniques in enhancing adaptive educational systems and their benefits for personalized learning.