Personalized curriculum sequencing utilizing modified item response theory for web-based instruction

Curriculum sequencing is an important research issue for Web-based instruction systems because no fixed learning pathway will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanism to assist on-line Web-based learning and adaptively provide learning pathways. However, although most personalized systems consider learner preferences, interests and browsing behavior in providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended courseware are matched to each other or not. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning, thus reducing learning effect. Besides, the problem of concept continuity of learning pathways also needs to be considered while implementing personalized curriculum sequencing. Smoother learning pathways increase learning effect, avoiding unnecessarily difficult concepts. This paper presents a prototype of personalized Web-based instruction system (PWIS) based on the proposed modified Item Response Theory (IRT) to perform personalized curriculum sequencing through simultaneously considering courseware difficulty level, learner's ability and the concept continuity of learning pathways during learning. In the proposed modified IRT, the information function is revised to consider the concept continuity of learning pathway as well as considering the difficulty level of courseware and individual learner ability. Experiment results indicate that applying the proposed modified IRT for Web-based learning can construct suitable learning pathway to learners for personalized learning, and help them to learn more effectively.     

Personalized e-learning system using Item Response Theory

Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.     

Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

Design a personalized e-learning system based on item response theory and artificial neural network approach

In web-based educational systems the structure of learning domain and content are usually presented in the static way, without taking into account the learners’ goals, their experiences, their existing knowledge, their ability (known as insufficient flexibility), and without interactivity (means there is less opportunity for receiving instant responses or feedbacks from the instructor when learners need support). Therefore, considering personalization and interactivity will increase the quality of learning. In the other side, among numerous components of e-learning, assessment is an important part. Generally, the process of instruction completes with the assessment and it is used to evaluate learners’ learning efficiency, skill and knowledge. But in web-based educational systems there is less attention on adaptive and personalized assessment. Having considered the importance of tests, this paper proposes a personalized multi-agent e-learning system based on item response theory (IRT) and artificial neural network (ANN) which presents adaptive tests (based on IRT) and personalized recommendations (based on ANN). These agents add adaptivity and interactivity to the learning environment and act as a human instructor which guides the learners in a friendly and personalized teaching environment.     

PC<Superscript>2</Superscript>PSO: personalized e-course composition based on Particle Swarm Optimization

This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called PC²PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The PC²PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. PC²PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the PC²PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process.     

Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all learners in a class based on their professional disciplines in the traditional classroom learning. However, the requirements of individual learners are frequently ignored in the traditional classroom learning. Compared to the conventional classroom learning, individual learners are the focus in web-based learning environments and many web-based learning systems provide personalized learning mechanisms for individual learners. One key problem is that learners have to frequently interact with web-based learning systems even though they lack instructors to monitor their learning attitudes and behavior during learning processes. Hence, a learner’s ability to self-regulated learning is clearly an important factor affecting learning performance in a web-based learning environment. Self-regulated learning is a goal-oriented learning strategy that is very suited to self-managed learning to promote learning performance of individual learners in a web-based learning environment. However, how to assist learners in cultivating self-regulated learning abilities efficiently is an important research issue in the self-regulated learning field. This study presents a novel personalized e-learning system with self-regulated learning assisted mechanisms that help learners enhance their self-regulated learning abilities. The proposed self-regulated learning mechanisms assist learners in becoming lifelong learners who have autonomous self-regulated learning abilities. Additionally, four self-regulated learning types, based on a self-regulated learning competence index and self-regulated learning performance index, are also proposed. Experimental results demonstrate that the proposed self-regulated learning assisted mechanisms aid learners by speeding up their acquisition of self-regulated learning abilities in a personalized e-learning system, and help their learning performance.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Since learning English is very popular in non-English speaking countries, developing modern assisted-learning tools that support effective English learning is a critical issue in the English-language education field. Learning English involves memorization and practice of a large number of vocabulary words and numerous grammatical structures. Vocabulary learning is a principal issue for English learning because vocabulary comprises the basic building blocks of English sentences. Therefore, many studies have attempted to improve the efficiency and performance when learning English vocabulary. With the accelerated growth in wireless and mobile technologies, mobile learning using mobile devices such as PDAs, tablet PCs, and cell phones has gradually become considered effective because it inherits all the advantages of e-learning and overcomes limitations of learning time and space that limit web-based learning systems. Therefore, this study presents a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycle, which recommends appropriate English vocabulary for learning according to individual learner vocabulary ability and memory cycle. The proposed system has been successfully implemented on personal digital assistant (PDA) for personalized English vocabulary learning. The experimental results indicated that the proposed system could obviously promote the learning performances and interests of learners due to effective and flexible learning mode for English vocabulary learning.     

An attribute-based ant colony system for adaptive learning object recommendation

Teachers usually have a personal understanding of what “good teaching” means, and as a result of their experience and educationally related domain knowledge, many of them create learning objects (LO) and put them on the web for study use. In fact, most students cannot find the most suitable LO (e.g. learning materials, learning assets, or learning packages) from webs. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and to adaptively provide learning paths. However, although most personalized learning mechanism systems neglect to consider the relationship between learner attributes (e.g. learning style, domain knowledge) and LO’s attributes. Thus, it is not easy for a learner to find an adaptive learning object that reflects his own attributes in relationship to learning object attributes. Therefore, in this paper, based on an ant colony optimization (ACO) algorithm, we proposed an attributes-based ant colony system (AACS) to help learners find an adaptive learning object more effectively. Our paper makes three critical contributions: (1) It presents an attribute-based search mechanism to find adaptive learning objects effectively; (2) An attributes-ant algorithm was proposed; (3) An adaptive learning rule was developed to identify how learners with different attributes may locate learning objects which have a higher probability of being useful and suitable; (4) A web-based learning portal was created for learners to find the learning objects more effectively.     

浅谈主题图在网络课件中的应用

网络课件作为一种新型的学习资源被越来越多的人使用,但目前的网络课件多数以单一的知识罗列为目标,并不能真正地体现学习者对网络课件的个性化需求。因此,提出一种基于主题图的导航式新型网络学习课件,试图从不同的知识粒度和更符合人类认知的网状知识表现形式来展现教育资源,提高教育资源的学习效果,更好地为学习者服务。     

Mobile formative assessment tool based on data mining techniques for supporting web-based learning

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final learning outcomes, without concerning the learning processes of learners. With the evolution of learning technology, the use of learning portfolios in a web-based learning environment can be beneficially adopted to record the procedure of the learning, which evaluates the learning performances of learners and produces feedback information to learners in ways that enhance their learning. Accordingly, this study presents a mobile formative assessment tool using data mining, which involves six computational intelligence theories, i.e. statistic correlation analysis, fuzzy clustering analysis, grey relational analysis, K-means clustering, fuzzy association rule mining and fuzzy inference, in order to identify the key formative assessment rules according to the web-based learning portfolios of an individual learner for the performance promotion of web-based learning. Restated, the proposed method can help teachers to precisely assess the learning performance of individual learner utilizing only the learning portfolios in a web-based learning environment. Hence, teachers can devote themselves to teaching and designing courseware, since they save a lot of time in measuring learning performance. More importantly, teachers can understand the main factors influencing learning performance in a web-based learning environment based on the interpretable learning performance assessment rules obtained. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results and the factor analysis provides simple and clear learning performance assessment rules. Furthermore, the proposed learning feedback with formative assessment can clearly promote the learning performances and interests of learners.     

基于粒子群优化算法的学习资源推荐方法

针对学习者的能力、学习目标、学习时间的个别差异,提出以粒子群优化（PSO）算法为基础的学习资源推荐方法,提供每位学习者个性化的数字课程。综合概念图和知识结构相关理论构建知识点网络结构图,运用项目反应理论（IRT）分析不同学习者的学习目标和能力程度,再应用PSO算法从多样性的学习资源中挑选学习内容,形成个性化的课程推荐给学生。初始化粒子时考虑学习者的学习时间上下限,过滤掉一些不必要的粒子来提高算法效率,在确定最优解位置时,使用Sigmoid函数修正粒子更新速度,保证其在有效范围内。实验结果表明,随着迭代次数增加,所推荐的内容与学习者预定目标差异为0,挑选出的课程与学习者能力差异为0.6,整体差异为0.25,说明所使用的方法具有较好的收敛性,推荐的学习资源能够满足学习者要求。     

A method toward dynamic e-learning services modeling and the cooperative learning mechanism

The advanced information technologies have made it possible for individuals to carry out cooperative learning efficiently and effectively from anywhere and at any time. To capitalize on the individual need and address the issues associated with the late entry into the e-learning area, it has great significance to study the service mechanism of CSCL on e-learning service and e-learning service computing modeling. This paper proposes an e-learning service model supporting for the life-cycle process management. The proposed model is developed by considering the learner’s behaviours during e-learning services, the scheduling policies, and the monitoring mechanism of learning activities. Business process modeling for e-learning services can be taken according to the study ordering of the knowledge points by using workflow modeling technology and process enactment mechanism. The overall life-cycle process management of knowledge is addressed by combining knowledge product modeling, knowledge resource modeling, and credit polices for member selection in research team by considering trust value of learners, advisers and providers in e-learning services. The proposed method can be used for supporting the sustainable development of e-learning services from planning and design, organizing e-learning process, maintenance of the e-learning process, to process improvement, as well as to support learners and advisers to effectively complete innovative team study and complex computation study. Lastly, an extended topic map tool has been developed by adding a knowledge requirement level and an information extraction tool to validate the proposed methodology. These tools can used to guide learners to concentrate on the required knowledge topics and drive knowledge providers to redevelop outdated knowledge hierarchy.     

Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT

In this study, an innovative adaptive and intelligent web based e-learning system, UZWEBMAT (Turkish abbreviation of Adaptive and INtelligent WEB based MAThematics teaching–learning system) was designed, developed and implemented. This e-learning system was intended for learning and teaching secondary school level permutation-combination-binomial expansion and probability subjects. Content which was prepared according to Turkish curriculum for secondary school mathematics course was transformed into learning objects in three different ways in accordance with VAK (Visual–Auditory–Kinesthetic) learning styles. Primary/secondary/tertiary learning styles of learners registering the system are determined and each learner receives the content appropriate for his/her dominant learning style. Also, they can be directed to contents of other styles according to their performances thanks to an expert system. Learning objects constituting the content were prepared according to constructivist approach. An active role for the learner was the purpose. Tips and intelligent solution supports within the learning objects were presented with expert system support to the learners. With this structure, UZWEBMAT bears the characteristics of intelligent tutoring system as well as an adaptive e-learning environment. All the movements of learners studying with UZWEBMAT are recorded and the necessary information is reported to both learners and teachers in a visualized way.     

Learning Java: a programmed instruction approach using Applets

A programmed instruction approach to knowledge acquisition is operationalized by student–teacher interactions that involve monitoring and managing the moment-by-moment progress of a learner throughout the process of achieving a criterion of mastery in a knowledge domain. The teacher is generic and may include another person, a structured text, or a computer. When the steps and increments leading to a task's completion and mastery can be enumerated, the process of interaction-based learning lends itself to implementation with computer-based tutoring systems. The present paper describes a computer-based programmed instruction tutoring system that teaches a learner how to write a simple Java™ computer program. The system is based on a series of Java Applets, which are computer programs that are downloaded from a network server and executed by a client browser such as Netscape®. The use of Java Applets makes the tutoring system available to learners over the World Wide Web. Performance acquisition data are presented to show an individual learner's progression to task completion and mastery using the tutoring system. Survey data are presented to show the subjective responses of a class of students to the use of the tutoring system. The adoption of this computer-based tutoring system is justified as one component within a personalized system of instruction that also includes lectures and collaborative learning experiences.     

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.     

Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system

Advances in wireless networking, mobile broadband Internet access technology as well as the rapid development of ubiquitous computing means e-learning is no longer limited to certain settings. A ubiquitous learning (u-learning) system must however not only provide the learner with learning resources at any time and any place. However, it must also actively provide the learner with the appropriate learning assistance for their context to help him or her complete their e-learning activity. In the traditional e-learning environment, the lack of immediate learning assistance, the limitations of the screen interface or inconvenient operation means the learner is unable to receive learning resources in a timely manner and incorporate them based on the actual context into the learner’s learning activities. The result is impaired learning efficiency. Though developments in technology have overcome the constraints on learning space, an inability to appropriately exploit the technology may make it an obstacle to learning instead. When integrating the relevant information technology to develop a u-learning environment, it is therefore necessary to consider the personalization requirements of the learner to ensure that the technology achieves its intended result. This study therefore sought to apply context aware technology and recommendation algorithms to develop a u-learning system to help lifelong learning learners realize personalized learning goals in a context aware manner and improve the learner’s learning effectiveness.     

An intelligent learning diagnosis system for Web-based thematic learning platform

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners’ ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners’ past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners’ final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based “theme-based learning” model.     

一种基于模糊理论的个性化网络学习系统

在信息社会中,学习已经成为人们日常生活中很重要的组成部分。网络学习是一种集计算机网络技术、卫星通信技术和多媒体技术于一体的学习方式,它对人们的终身学习起到非常重要的作用。提出了一种基于模糊集理论的个性化网络学习系统,利用模糊集理论知识构建和描述学习资源数据库模型和学习者数据库模型。这种系统既能形成描述网络课程知识的模糊结构图,又能针对不同的学习者形成学习者的模糊结构子图,并能根据学习者的学习进度和能力水平,提供不同的学习内容和导航策略,从而满足个性化网络学习的需求。     

Developing argumentation processing agents for computer-supported collaborative learning

In this study, an intelligent argumentation processing agent for computer-supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. The proposed argumentation processing agent then scrutinizes each learner’s learning portfolio on e-learning platform and automatically issues feedback messages in case devious argument or abnormal behavior that is unfitted to the learners’ learning style is detected. The Moodle (http://moodle.org), an open source software e-learning platform, is used to establish the cooperative learning environment for this study. The experimental results revealed that the learners benefited by the argumentation activity with the assistance of the proposed learning style aware argumentation processing agent.