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.     

语音词汇网单词联想记忆

语音联想记忆是一种高效的记忆方法。为了给学习者提供语音联想的素材,引导学习者进行语音联想,熟悉读音规则,加深对单词拼写和发音的记忆,帮助学习者建立字母组合与相关发音间的双向认知,提出一种基于语音的词汇网。语音词汇网是基于常见字母组合和单词读音之间的差异构建的,因此其中既包含了语音近似度信息,也包含了一定的单词结构信息。利用该网络,学习系统不但可以实现语音联想功能,还能提供语音方面的相关统计信息。语音词汇网的引入能进一步完善单词学习系统的联想记忆功能。     

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.     

Measuring web usability using item response theory: Principles, features and opportunities

Usability is considered a critical issue on the web that determines either the success or the failure of a company. Thus, the evaluation of usability has gained substantial attention. However, most current tools for usability evaluation have some limitations, such as excessive generality and a lack of reliability and validity. The present work proposes the construction of a tool to measure usability in e-commerce websites using item response theory (IRT). While usability issues have only been considered in theoretical or empirical contexts, in this study, we discuss them from a mathematical point of view using IRT. In particular, we develop a standardised scale to measure usability in e-commerce websites. This study opens a new field of research in the ergonomics of interfaces with respect to the development of scales using IRT.     

Using RSS to support mobile learning based on media richness theory

With the rapid development of mobile technologies, mobile learning has become a new trend in education. A better understanding of how to effectively use communication technologies to improve mobile learning is important. The purpose of this paper is to evaluate the media richness of various message delivery methods in the proposed m-learning environment based on media richness theory. Regarding the implications of the media richness theory, this study has identified four factors to evaluate a content in respect to the media richness among SMS, Email, and RSS: timeliness, richness, accuracy and adaptability. By the repeated-measures one-way ANOVA analysis, the results show that: (1) SMS has better performance than Email and RSS on content timeliness; thus SMS may be appropriate for immediate information delivery such as notifying or reminding of some time-sensitive matters; (2) Email has better performance than SMS and RSS on content richness and so may be applied in exhaustive information delivery; (3) RSS has better performance than SMS and Email on content accuracy and adaptability; thus RSS is more appropriate for supporting various front-end mobile devices to access and present the content in a mobile learning environment. According to the results, this study suggests developer and designer of an m-learning environment could adopt suitable information delivery medium to support the corresponding learning activities in a mobile learning environment; moreover, current general e-learning systems, particularly those intending to provide a mobile learning environment, can take advantage of RSS techniques to support mobile access and achieve the goal of mobile learning anytime and anywhere.     

Improving the effectiveness of English vocabulary review by integrating ARCS with mobile game‐based learning

Memorizing English vocabulary is often considered uninteresting, and a lack of motivation exists during learning activities. Moreover, most vocabulary practice systems automatically select words from articles and do not provide integrated model methods for students. Therefore, this study constructed a mobile game‐based English vocabulary practice system, which imitates the popular block elimination game and combines test items of article, difficulty, and teacher model in accordance with curriculum objectives and demands. The participants were first‐year students and classified into traditional group (Group T) and game‐based group (Group G), and learning effectiveness and motivation were analysed in this study. According to statistical analysis results, students who used the game‐based vocabulary practice system, which could captivate their attention and interest, had higher learning effectiveness and provided positive feedback of learning motivation. Furthermore, English teachers could select teaching materials that are consistent with the features, knowledge, and cultural background of learners to improve the relevance dimension of learning motivation.     

Virtual reality in problem-based learning contexts: Effects on the problem-solving performance,vocabulary acquisition and motivation of English language learners

Learning a foreign language requires interaction with language input while involved in a task. Given that problem-based learning (PBL) offers hands-on application in realistic contexts, and that virtual reality (VR) enables learners to interact with multiple modalities of information, this study examines how the integration of VR technology into PBL contexts affects students' motivation for, problem-solving during, and vocabulary acquisition in learning English as a foreign language (EFL). A total of 84 engineering majors who enrolled in a course of English for specific purposes were randomly assigned to either an experimental group or a control group. Students in the experimental group participated in a VR-assisted PBL context, in which they were to view a PBL scenario using VR technology and then to create VR videos about solving the given problems. Those in the control group participated in a PBL context without the use of VR technology for viewing and solving the identical scenario. After the intervention, all the students wrote a problem-solving analysis, took a vocabulary knowledge test, completed a learning motivation questionnaire, and participated in individual interviews. The results showed that the students in the experimental group significantly outperformed those in the control group in terms of vocabulary acquisition, and were more motivated to learn English related to their future careers, whereas there was no significant difference in the problem-solving performance of the two groups. Implications of these findings highlight the value of engaging EFL learners in immersive environments for contextualized learning through the integrated use of VR and PBL.     

The added value of a gaming context and intelligent adaptation for a mobile learning application for vocabulary learning

Two groups participated in a study on the added value of a gaming context and intelligent adaptation for a mobile learning application. The control group worked at home for a fortnight with the original Mobile English Learning application (MEL-original) developed in a previous project. The experimental group worked at home for a fortnight with MEL-enhanced, the original application embedded in an adventure game and augmented with intelligent adaptation. Two learning themes were used: Zoo animals and Neighbourhood. Both groups attended lessons at school on Zoo Animals and Neighbourhood during the same periods they were allowed to work with the application at home. A pre- and post-test were conducted to establish the initial vocabulary knowledge and the knowledge acquired during the learning phase. The main results indicated that the students in the experimental condition (MEL-enhanced) outperformed the children from the control group (MEL-original), although the former group did not spend more time with the learning material than the latter, and that the students in the experimental group valued MEL-enhanced more than the children from the control group valued MEL-original.     

Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale

In the past decade, several studies have measured ICT competences from the perspective of ICT self-efficacy. Such indirect measurements tend to have validity problems, as they depend on the pupils' ability to judge their own ICT competences. This study outlines the development of a performance-based digital test and the validation of a direct measure of ICT competence through the use of item response theory (IRT). More specifically, the test and the developed measure focus on primary-school pupils' proficiency in digital information processing and communication. 56 Items were administered to 560 pupils at the end of their primary-school education (age between 10.79 and 13.85 years old). The items were controlled for dimensionality, model-data fit, local item dependence and monotonicity. The final measure contains 27 items that refer to retrieving and processing digital information, and communication with a computer. The results indicate that the instrument is particularly reliable for low and median ability levels. Further refinement and possible future use of the instrument is discussed.     

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item‐based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item‐based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item‐based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.     

An investigation of mobile learning readiness in higher education based on the theory of planned behavior

This study investigated the current state of college students' perceptions toward mobile learning in higher education. Mobile learning is a new form of learning utilizing the unique capabilities of mobile devices. Although mobile devices are ubiquitous on college campuses, student readiness for mobile learning has yet to be fully explored in the United States. The paper describes a conceptual model, based on the theory of planned behavior (TPB), which explains how college students' beliefs influence their intention to adopt mobile devices in their coursework. Structural equation modeling was used to analyze self-report data from 177 college students. The findings showed that the TPB explained college students' acceptance of m-learning reasonably well. More specifically, attitude, subjective norm, and behavioral control positively influenced their intention to adopt mobile learning. The results provide valuable implications for ways to increase college students' acceptance of mobile learning.     

Promoting contextual vocabulary learning through an adaptive computer‐assisted EFL reading system

The study developed an adaptive computer‐assisted reading system and investigated its effect on promoting English as a foreign language learner‐readers' contextual vocabulary learning performance. Seventy Taiwanese college students were assigned to two reading groups. Participants in the customised reading group read online English texts, each of which was customised by the developed system to offer immediate and repeated meetings with previously encountered unknown words, while participants in the typical reading group read online texts without control on unknown word recurrence. After the 4‐week online reading treatment, the two groups of learners were tested by online immediate and delayed vocabulary tests, and the students from the customised reading group were also required to complete the questionnaire regarding system use. The results showed that the vocabulary mean scores from both immediate and delayed testing demonstrated significantly better results in word gain and word retention with the customised reading group and that the adaptive reading system was appealing for the students. The study demonstrated that by providing customised reading with word recurrence specific to individuals' unknown words, this adaptive computer‐assisted English as a foreign language reading system creates a more favourable condition for foreign language vocabulary growth.     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

Internet cognitive failure and fatigue relevant to learners' self‐regulation and learning progress in English vocabulary with a calibration scheme

To determine the factors of learning effectiveness in English vocabulary learning when using a calibration scheme, this study developed a freshman English mobile device application (for iPhone 4) for students with low levels of English proficiency to practise vocabulary in the beginning of their Freshman English course. Data were collected and validated from 243 subjects for confirmatory factor analysis and structural equation modeling. The findings revealed that Internet cognitive failure (i.e., trait cognitive disability) was positively correlated to Internet cognitive fatigue (i.e., state cognitive disability). Both types of Internet cognitive disability were negatively correlated to self‐regulation in English vocabulary learning (SREVL). SREVL was positively correlated to the degree of learning improvement. The findings implied that the use of a calibration design for mobile English vocabulary learning could benefit students with low levels of Internet cognitive disability but high levels of SREVL.     

A B-Tree index extension to enhance response time and the life cycle of flash memory

Flash memory has critical drawbacks such as long latency of its write operation and a short life cycle. In order to overcome these limitations, the number of write operations to flash memory devices needs to be minimized. The B-Tree index structure, which is a popular hard disk based index structure, requires an excessive number of write operations when updating it to flash memory. To address this, it was proposed that another layer that emulates a B-Tree be placed between the flash memory and B-Tree indexes. This approach succeeded in reducing the write operation count, but it greatly increased search time and main memory usage. This paper proposes a B-Tree index extension that reduces both the write count and search time with limited main memory usage. First, we designed a buffer that accumulates update requests per leaf node and then simultaneously processes the update requests of the leaf node carrying the largest number of requests. Second, a type of header information was written on each leaf node. Finally, we made the index automatically control each leaf node size. Through experiments, the proposed index structure resulted in a significantly lower write count and a greatly decreased search time with less main memory usage, than placing a layer that emulates a B-Tree.     

基于强化学习方法的访存调度算法

在现代处理器中,存储控制器是处理器芯片对片外存储器进行访问的管理者和执行者,其中对访存过程的调度算法会对实际访存性能产生十分重要的影响。针对已有调度算法在不同负载特征下自适应性不足的问题,提出了一种基于强化学习方法的ALHS算法,通过对访存调度中页命中优先时的连续页命中上限次数进行自适应调整,习得最优策略。多种不同典型访存模式的模拟结果显示,相比传统的FR-FCFS,ALHS算法运行速度平均提升了10.98%,并且可以获得近似于最优策略的性能提升,表明该算法能够自主探索环境并自我优化。     

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Android的应用英语移动学习软件的设计与实现

智能手机的普及使移动学习成为一种全新的学习方式,越来越受到研究者与学习者的关注。调查显示,移动学习对英语学习特别是应用英语的学习有良好的效果。基于Android设计开发的英语学习软件包含"实用英语"、"词汇学习"、"词典翻译"、"休闲娱乐"、"交流互动"五大功能模块,能够帮助学生利用碎片时间学习英语,基于场景的实用英语学习模式能有效提高学生的英语应用水平。文章主要阐述了在Android平台下移动英语学习软件的设计开发过程。     

Personalized course generation and evolution based on genetic algorithms

Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.