Handling linguistic web information based on a multi‐agent system

Much information over the Internet is expressed by natural languages. The management of linguistic information involves an operation of comparison and aggregation. Based on the Ordered Weighted Averaging (OWA) operator and modifying indexes of linguistic terms (their indexes are fuzzy numbers on [0,T] ? R⁺), new linguistic aggregating methods are presented and their properties are discussed. Also, based on a multi‐agent system and new linguistic aggregating methods, gathering linguistic information over the Internet is discussed. Moreover, by fixing the threshold α, “soft filtering information” is proposed and better Web pages (or documents) that the user needs are obtained. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 435–453, 2007.     

融入学习者模型在线学习资源协同过滤推荐方法

在线教育存在“信息迷航”问题,而传统的信息推荐方法往往忽视教育的主体—学习者的特征。本文依据教育教学理论,根据在线教育平台中的学习者相关数据,研究构建了适用于在线学习资源个性化推荐的学习者模型。以协同过滤推荐方法为切入点,融合学习者模型中的静态特征和动态特征对协同过滤方法进行改进,建立融入学习者模型的在线学习资源协同过滤推荐方法。以2020年3~7月时间段的东北石油大学“C程序设计”课程学生的真实学习数据和行为数据为数据集,对本文提出的方法进行验证和对比,最后证明本文提出的方法在性能上均优于对比方法。     

Mining students activities from a computer supported collaborative learning system based on peer to peer network

As Information & Communication Technology (ICT) is rapidly evolved, educational paradigms have been changing. The ultimate goal of education with the aid of ICT is to provide customized training for learners to improve the effectiveness of their learning at anytime and anywhere. In the online learning environment where the Internet, mobile devices, peer-to-peer (P2P) and the cloud technology are leveraged, all the information in learning activities is converted into digital data and stored in the Computer Supported Collaborative Learning (CSCL) system. The data in the CSCL system contains various learners’ information including the learning objectives, learning preferences, competences and achievements. Thus, by analyzing the activity information of learners in an online CSCL system, meaningful and useful information can be extracted and provided for learners, teachers and administrators as feedback. In this paper, we propose a learner activity model that represents the learner’s activity information stored in a CSCL system. As for the proposed learner activity model, we classified the learning activities in a CSCL system into three categories: vivacity, learning and relationship; then we created quotients to represent them accordingly. In addition, we developed a CSCL System, which we termed as COLLA, applied the proposed learner activity model and analyzed the results.     

A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.     

The politeness effect: Pedagogical agents and learning outcomes

Pedagogical agent research seeks to exploit Reeves and Nass's media equation theory, which holds that users respond to interactive media as if they were social actors. Investigations have tended to focus on the media used to realize the pedagogical agent, e.g., the use of animated talking heads and voices, and the results have been mixed. This paper focuses instead on the manner in which a pedagogical agent communicates with learners, i.e., on the extent to which it exhibits social intelligence. A model of socially intelligent tutorial dialog was developed based on politeness theory, and implemented in an agent interface within an online learning system called virtual factory teaching system. A series of Wizard-of-Oz studies was conducted in which subjects either received polite tutorial feedback that promotes learner face and mitigates face threat, or received direct feedback that disregards learner face. The polite version yielded better learning outcomes, and the effect was amplified in learners who expressed a preference for indirect feedback, who had less computer experience, and who lacked engineering backgrounds. These results confirm the hypothesis that learners tend to respond to pedagogical agents as social actors, and suggest that research should focus less on the media in which agents are realized, and place more emphasis on the agent's social intelligence.     

协作学习中基于协同过滤的学习资源推荐研究

符合学习者特征的学习资源对于提高协作学习效率具有重要的影响。但是传统的学习资源推荐,没有充分考虑学习者、学习资源的特征和高效的推荐算法。针对上述问题,提出了基于协同过滤的学习资源推荐算法,根据学习者学习特征、学习资源特征和学习者对学习资源历史评价信息,采用协同过滤推荐算法,实现学习资源推荐。首先,通过学习者特征和学习资源的评分,寻找相似学习者并计算学习资源预测评分,然后根据该评分值和学习资源与学习者匹配度推荐学习资源,从而为学习者推荐符合自己兴趣爱好最合适的学习资源。实验结果表明该算法在个性化学习资源推荐的准确性上优于传统算法。     

Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour

Recently, research in individual differences and in particular, learning and cognitive style, has been used as a basis to consider learner preferences in a web-based educational context. Modelling style in a web-based learning environment demands that developers build a specific framework describing how to design a variety of options for learners with different approaches to learning. In this paper two representative examples of educational systems, Flexi-OLM and INSPIRE, that provide learners a variety of options designed according to specific style categorisations, are presented. Experimental results from two empirical studies performed on the systems to investigate learners' learning and cognitive style, and preferences during interaction, are described. It was found that learners do have a preference regarding their interaction, but no obvious link between style and approaches offered, was detected. Derived from an examination of this experimental data, we suggest that while style information can be used to inform the design of learning environments that accommodate learners' individual differences, it would be wise to recommend interactions based on learners' behaviour. Learning environments should allow learners or learners' interaction behaviour to select or trigger the appropriate approach for the particular learner in the specific context. Alternative approaches towards these directions are also discussed.     

Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom

Teachers are important social agents who shape the quantity and quality of students' self-directed use of technology for learning outside the classroom. This study aimed to model the influence of teacher behaviors on learners' self-directed technology use. A conceptual model of three types of teacher support (affection support, capacity support and behavior support) that were reported to influence students' self-directed technology use for learning outside the classroom was generated based on interviews with 15 undergraduate foreign language learners. One hundred and sixty undergraduate foreign language learners were then surveyed to test the conceptual model. The path analysis of the survey data suggested that affection support influenced learner self-directed technology use through strengthened perceived usefulness, and that capacity support and behavior support influenced learner self-directed technology use through enhanced facilitating conditions and computer self-efficacy. The research findings highlight the importance of raising teachers' awareness of the different roles they can play and of enhancing their abilities to perform a combination of the roles to promote learner self-directed use of technology for learning outside the classroom.     

Variations on U-shaped learning

The paper deals with the following problem: is returning to wrong conjectures necessary to achieve full power of algorithmic learning? Returning to wrong conjectures complements the paradigm of U-shaped learning when a learner returns to old correct conjectures. We explore our problem for classical models of learning in the limit from positive data: explanatory learning (when a learner stabilizes in the limit on a correct grammar) and behaviourally correct learning (when a learner stabilizes in the limit on a sequence of correct grammars representing the target concept). In both cases we show that returning to wrong conjectures is necessary to achieve full learning power. In contrast, one can modify learners (without losing learning power) such that they never show inverted U-shaped learning behaviour, that is, never return to old wrong conjecture with a correct conjecture in-between. Furthermore, one can also modify a learner (without losing learning power) such that it does not return to old “overinclusive” conjectures containing non-elements of the target language. We also consider our problem in the context of vacillatory learning (when a learner stabilizes on a finite number of correct grammars) and show that each of the following four constraints is restrictive (that is, reduces learning power): the learner does not return to old wrong conjectures; the learner is not inverted U-shaped; the learner does not return to old overinclusive conjectures; the learner does not return to old overgeneralizing conjectures. We also show that learners that are consistent with the input seen so far can be made decisive: on any text, they do not return to any old conjectures—wrong or right.     

Mobile Learning projects – a critical analysis of the state of the art

This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control, communication, subject and objective. Although a significant number of projects have ventured to incorporate the physical context into the learning experience, few projects include a socializing context. Tool support ranges from pure content delivery to content construction by the learners. Although few projects explicitly discuss the Mobile Learning control issues, one can find all approaches from pure teacher control to learner control. Despite the fact that mobile phones initially started as a communication device, communication and collaboration play a surprisingly small role in Mobile Learning projects. Most Mobile Learning projects support novices, although one might argue that the largest potential is supporting advanced learners. All results show the design space and reveal gaps in Mobile Learning research.     

基于行为序列分析的学习资源推荐算法研究

利用数据挖掘技术分析网络学习行为数据可以挖掘出其隐含的行为规律特征,为学习者提供个性化的学习资源服务。针对现有的数据挖掘算法在对网络学习行为数据进行分析时普遍存在模型适用性不高的问题,提出了一种基于行为序列分析的学习资源推荐算法。首先,提出行为序列及其相关概念的定义,并提出行为序列相似度计算方法;然后提出基于行为序列相似度的协同过滤推荐算法,计算学习者相似度并为待推荐学习者生成学习资源推荐列表;接着给出基于学习风格的推荐方法,将学习者学习风格特征融入推荐过程;最后,给出基于行为序列分析的学习资源推荐算法的模型。提出的算法没有对行为序列的模式进行限制,具有较高的适用性,对深入研究网络学习行为序列数据为学习者提供个性化学习服务具有一定的借鉴作用。     

Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings

Mobile technologies can support learning across different contexts as their portability enables them to be used by the learner in whichever context she or he is in. They can be particularly beneficial in informal and semiformal contexts where learners have more control over their learning goals and where motivation is often high. Inquiries in informal contexts are likely to be personally relevant in terms of topics of interest and capitalise on learners' location as learners decide what, where, when and whether to learn. There is considerable interest in how such benefits can be harnessed for more formal learning and one challenge is how to make inquiries personally relevant in such contexts. However, there is little literature that considers the structure needed to support informal and semiformal inquiry learning. This paper contributes to that literature by examining dimensions for researchers and designers to consider investigating or developing support for inquiries in informal or semiformal settings.The paper examines two case studies of inquiry learning in contrasting settings in order to understand more about learner control and how technology can support learners' inquiries. Case study one considers the use of web based software to support science inquiry learning by 14–15 year olds in a semiformal context, whilst the second case study reports on informal adult learners using their own mobile technologies to learn about landscape. These case studies are compared and contrasted in terms of the dimensions of learner control, location of learning, and the different support mechanisms for inquiry learning and a framework is proposed for considering these dimensions.     

LIM-G: Learner-initiating instruction model based on cognitive knowledge for geometry word problem comprehension

Computer-assisted instruction systems have been broadly applied to help students solve math word problem. The majority of such systems, which are based on an instructor-initiating instruction strategy, provide pre-designed problems for the learners. When learners are asked to solve a word problem, the system will instruct the learners what to do. However, systems employing an instructor-initiating instruction strategy offer little help to advanced learners or to learners encountering problems that are not in the pre-designed database. Therefore, in this study, a learner-initiating instruction model (LIM-G) is proposed to help learners’ comprehension of geometry word problems. Geometry word problems are math word problems involving geometric concepts. Many researches indicate that learners encounter difficulties while comprehending math word problems. In this model, a learner can seek help with any geometry word problem he is interested in. Based on a learner-initiating instruction strategy, LIM-G first comprehends the problem and then gives the learner the telegraphic and diagrammatic representations of the problem, which are more intuitive to understand. For LIM-G, the comprehension mechanism plays a critical role in solving word problems. For this study, a system is built based on LIM-G. In this system, the cognitive knowledge needed for comprehending geometry word problem is constructed with an ontology-based tool called InfoMap. Using cognitive knowledge and frame-template structures, the system can extract the relevant concepts in geometry word problems for comprehension.     

Clustering web search results using fuzzy ants

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant‐based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 455–474, 2007.     

A multigranular linguistic content‐based recommendation model

Recommendation systems are a clear example of an e‐service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content‐based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content‐based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Machine Learning for Information Extraction in Informal Domains

We consider the problem of learning to perform information extraction in domains where linguistic processing is problematic, such as Usenet posts, email, and finger plan files. In place of syntactic and semantic information, other sources of information can be used, such as term frequency, typography, formatting, and mark-up. We describe four learning approaches to this problem, each drawn from a different paradigm: a rote learner, a term-space learner based on Naive Bayes, an approach using grammatical induction, and a relational rule learner. Experiments on 14 information extraction problems defined over four diverse document collections demonstrate the effectiveness of these approaches. Finally, we describe a multistrategy approach which combines these learners and yields performance competitive with or better than the best of them. This technique is modular and flexible, and could find application in other machine learning problems.     

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.     

基于认知诊断的协同过滤试题推荐

针对协同过滤忽略了学习者的知识点掌握情况（学习状态）,对个性化教育试题推荐中运用的协同过滤算法进行了一定改进研究,该推荐算法分为三个步骤：（1）结合认知诊断模型,对学习者所练习题目中反映的知识点掌握情况进行建模分析;（2）利用协同过滤算法,结合学习者的知识点掌握情况,来对学习者的表现情况进行相似度分析;（3）根据相似用户的历史行为数据和目标用户的知识点掌握状态,针对学习者的近邻用户进行试题推荐.该推荐办法借鉴了群体相似学习者的共性,也考虑到了个体学习者的独特性,结合二者来对学习者进行个性化试题推荐,保证了试题推荐的准确性和性能,在个性化教育系统中,结合认知诊断改进了原有的协同过滤算法来对试题做出推荐.