Data mining in course management systems: Moodle case study and tutorial

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.     

基于本体的Web挖掘在个性化E-learning系统中的应用

针对当前e-learning系统中存在着资源利用效率低和学习内容单一、个性化不足等问题,设计一个基于本体的web挖掘的个性化e-learning系统,通过应用web挖掘和本体技术,使得该系统能根据学习者的知识结构、学习目标、学习风格、偏好等特征信息提供适应学习者的教学方法和学习资源,营造个性化的网络学习环境。实验表明本体技术能明显改善挖掘效果,提高学习资源库的管理效率,有效促进学生的网络学习,满足学生个性化学习的需求,为系统的决策分析提供了智能的辅助手段。     

Personalized web-based tutoring system based on fuzzy item response theory

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner’s behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner’s cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner’s crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner’s non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner’s response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner’s uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.     

基于多维度推荐的智慧在线学习系统

针对目前在线学习系统中存在的不足,探讨如何有效地运用数据挖掘技术建立智慧的在线学习系统.从大量的用户数据中挖掘出关联关系,用以提供全面个性化、定制化的学习过程序列.利用数据挖掘着重发现用户与课程之间、课程与课程之间、用户与用户间的关联,形成一个多维度的网络.利用多维度推荐为用户推荐有价值的课程.实验表明多维度推荐具有良好的准确性和良好的用户体验.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

数据挖掘在个性化学习系统中的运用

介绍了一种个性化学习系统，它是远程教育的一种新模式。它针对不同学生的个体差异，为学生提供不同的学习资料。个性化的基础是记录学生的学习过程及学习结果，并对这些数据进行挖掘，分析出学生的个性化特征。该系统就是通过运用数据挖掘技术来研究学生的个性，制订适合学生个性的学习内容，为学生提供个性化服务。数据挖掘技术在网上学习系统中的应用提高了学习系统的个性化服务水平，为系统的决策分析提供了智能的辅助手段。     

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.     

基于Web的个性化学习系统的设计

为改善基于Web学习系统存在的不足,提出了一个基于数据挖掘技术的个性化学习系统模型,并详细描述了应用决策树及BP神经网络算法对个性化导航模块设计的方法.应用决策树方法,根据学生初始注册信息,为学生的学习能力进行分类;应用BP神经网络算法,对经过预处理的有用的教学数据进行挖掘,以得出学生对知识点的掌握情况;在分析对比学生的学习状态与课程要求的基础上为学生提供下一步学习的导航信息.基于该模型实现的个性化学习系统真正体现了因材施教的教育理念.     

The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning material.     

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.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

基于Web挖掘的个性化网络学习系统设计

设计了一种基于Web挖掘的个性化网络学习系统,该系统给出了Web内容挖掘、Web使用挖掘和Web结构挖掘的结果,并结合其推荐结果为学习者提供个性化的服务。并给出个性化推荐算法。     

Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation

Virtual learning environments (VLEs) developed under constructivism and embedded personalization learning functions have the potential to meet different requirements of different learners and thus increase e-Learning effectiveness. We formulated internal personalized learning mechanisms by implementing intelligent agents in a VLE under a constructivist learning model and further developed an e-learning effectiveness framework by integrating educational and IS theories. An empirical field experiment involving 228 university students was conducted. The findings suggested that personalized e-learning facilities enhance online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria.     

Construction safety training via e-Learning: Learning effectiveness and user satisfaction

In Taiwan, promoting knowledge of “Labor Safety” which relates to life and work right is very important. Safety training and learning effectiveness become essential issues of adult learning. To reduce the costs of educational training, enterprises have also started to aggressively introduce e-learning education training. Unlike the construction industry, few studies have investigated the effectiveness of e-learning and conventional learning. This study tested the effectiveness of the safety education to prevent falls by different learning modes used to assess safety behavior and learning effectiveness during the education training period. According to the average pass rate, satisfaction degree of course and total number of unsafe behavior, the e-learning mode improves learning effectiveness. Additionally, when the e-learning mode is introduced in the construction safety education training, the labor can use the teaching material more independently and multimedia system, such as animated teaching materials, case teaching, and repeated course learning, to reduce the error rate of operation, property loss rate, and light (heavy) injury. Under this condition, the e-learning mode is positively associated with the learning effectiveness of construction safety education training. High learning effectiveness promotes safe behavior during construction operations.     

A systematic study on attribute reduction with rough sets based on general binary relations

Attribute reduction is considered as an important preprocessing step for pattern recognition, machine learning, and data mining. This paper provides a systematic study on attribute reduction with rough sets based on general binary relations. We define a relation information system, a consistent relation decision system, and a relation decision system and their attribute reductions. Furthermore, we present a judgment theorem and a discernibility matrix associated with attribute reduction in each type of system; based on the discernibility matrix, we can compute all the reducts. Finally, the experimental results with UCI data sets show that the proposed reduction methods are an effective technique to deal with complex data sets.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

个性化课程学习模型的设计与实现

针对目前远程教育环境中,学习者缺乏个性化指导、学习效率低下的问题,提出了一种基于自组织社区的以CELTS为参考的个性化课程学习模型.该模型利用同伴之问的相似性,授课教师的权威性以及算法的针对性,通过推荐学习对象、学习路径和学习策略来引导学习者学习.该模型缩减了授课教师的工作量,实验结果表明,该模型能有效地提高学习者的学习质量和学习兴趣.     

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.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Data mining for adaptive learning in a TESL-based e-learning system

This study proposes an Adaptive Learning in Teaching English as a Second Language (TESL) for e-learning system (AL-TESL-e-learning system) that considers various student characteristics. This study explores the learning performance of various students using a data mining technique, an artificial neural network (ANN), as the core of AL-TESL-e-learning system. Three different levels of teaching content for vocabulary, grammar, and reading were set for adaptive learning in the AL-TESL-e-learning system. Finally, this study explores the feasibility of the proposed AL-TESL-e-learning system by comparing the results of the regular online course control group with the AL-TESL-e-learning system adaptive learning experiment group. Statistical results show that the experiment group had better learning performance than the control group; that is, the AL-TESL-e-learning system was better than a regular online course in improving student learning performance.