Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE

In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more 'learning-focused' paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learner' preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner's learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system's adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners' attitudes towards the proposed instructional design.     

Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior

In this paper, two experiments on the use of hypermedia environments for learning about probability theory are reported. In Experiment 1a it was tested whether multimedia design principles (multimedia principle, modality principle, redundancy principle) are valid in hypermedia environments, despite the fact that hypermedia offers more learner control than multimedia. The results showed only little evidence for this validity, although the hypermedia environment entailed only a rather low level of learner control. In Experiment 1b it was investigated how learner control affects performance and how its possible impact is moderated by learners’ prior knowledge. A high level of learner control positively affected the effectiveness of instruction only with regard to intuitive knowledge, but was at the same time accompanied by large increases in learning time, thereby rendering the instruction inefficient. Unexpectedly, effects of learner control were not moderated by students’ prior knowledge. The results imply that the idea to use multimedia design principles for hypermedia learning is too simple and that the benefits and drawbacks of learner control depend heavily on learning objectives and time constraints.     

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.     

Hypermedia Design Methodology in World Wide Web Applications

One approach to instruction based on a "learner-centered" view of learning is to provide rich environments in which learners can actually build their own knowledge. Therefore only educational software, carefully designed, can improve the efficiency of courseware. Within this framework, the World Wide Web provides a unique support for course material. Using a generalized hypermedia instructional design methodology, we integrated a hypermedia instructional module with simulation additional tools into an academic information system. This academic information system is a computer-supported environment in which collaborative discourse is the primary medium for knowledge advancement in the area of power electronics. It can be explored by learners in that they have both contextual access to knowledge displayed in a hypertext format and access to real experiences by means of simulation. In this article, we present an object-orientated approach that integrates the complete graphic user interface development.     

Learning styles and cognitive traits – Their relationship and its benefits in web-based educational systems

Different learners have different needs; they differ, for example, in their learning goals, their prior knowledge, their learning styles, and their cognitive abilities. Adaptive web-based educational systems aim to cater individual learners by customizing courses to suit their needs. In this paper, we investigate the benefits of incorporating learning styles and cognitive traits in web-based educational systems. Adaptivity aspects based on cognitive traits and learning styles enrich each other, enabling systems to provide learners with courses which fit their needs more accurately. Furthermore, consideration of learning styles and cognitive traits can contribute to more accurate student modelling. In this paper, the relationship between learning styles, in particular the Felder–Silverman learning style model (FSLSM), and working memory capacity, a cognitive trait, is investigated. For adaptive educational systems that consider either only learning styles or only cognitive traits, the additional information can be used to provide more holistic adaptivity. For systems that already incorporate both learning styles and cognitive traits, the relationship can be used to improve the detection process of both by including the additional information of learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.     

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.     

Bootstrapping in a language learning environment

Abstract   This paper addresses a fundamental dilemma in the design of intelligent language learning environments: the more freedom a system offers to learners in the use of the target language, the more unwieldy the data is which the learners produce and the less able the system is to support inferences about learners from that data. It is shown how in a platform where learners and teachers interact, the teachers' feedback which is archived in the system and indexed to the learners' target language production can constitute affordances that support a process of bootstrapping from raw language output to potential insights into the learners' interlanguage and gaps in their grasp of the target language. The approach is illustrated with three types of learner errors uncovered in the corpus of learner English through this bootstrapping heuristic.     

视觉化协同超媒体及其在合作学习中的应用

在分布式合作学习环境中,学生和老师需要一个共享的信息空间去与学习材料交互作用,并从中知晓合作者的存在及其活动.学生和老师还需要一些协同和通信手段去协调他们共同的活动.为此,提出了一种视觉化协同超媒体技术.它可以提供所需的共享信息空间.在此空间中,形象的超媒体对象既可以表示学习材料(学习内容),又可以表示学习过程(学习方法).这种协同超媒体集成了灵活的过程支持、通信渠道以及文件处理功能.因而,它可以支持学生(和老师)按一定的学习方法并通过文字或声音渠道去协调他们共同的学习活动.这种协同超媒体可以通过万维网(浏览器)访问.为了揭示这种技术的实用性,还给出了一个案例和一些用户体验.     

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.     

An AI-based open recommender system for personalized labor market driven education

Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.     

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.     

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.     

The relationship between the learning strategies and learning styles in a hypermedia environment

The relationship between hypermedia-assisted instruction and learners' different learning styles has prompted tremendous interest in educators because accommodating learners' different needs is one of the promises hypermedia technology is believed to hold. This study examined the different learning strategies by Field-independent (FI) people and Fielddependent (FD) people in a hypermedia assisted language learning setting. Sixty-three college, international students participated in the study. Findings that describe the different types of media, tools, and learning aids chosen by the FI and FD students were reported. Results from the study suggested that different learning style groups employed different learning strategies in accomplishing the same task. The results of the study indicated that the hypermedia technology has the potential to accommodate learners with different needs through its rich environment.     

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。     

Cognitive tools,individual differences,and group processing as mediating factors in a hypermedia environment

Much research exists on how problem-solving in hypermedia is assisted through the use of navigational aids. However, as hypermedia becomes more pedagogically rich, the structural characteristics of hypermedia no longer serve merely as navigational aids, but often as cognitive tools. When cognitive tools are embedded in problem-solving hypermedia environments, the objective is the continuous active manipulation of cognitive tools in the problem space in order to gather information, test hypotheses, and develop a solution. The purpose of this study was to investigate cognitive tools, individual differences, and group processing as mediating factors when learners were engaged in problem-solving in a hypermedia environment. We specifically examined group patterns of cognitive tool use to determine in what ways the group patterns of tool use may affect students’ individual performance and experience of the problem-solving process. The findings provided some evidence to support a more contextual approach to individual cognition and learning; and emphasized the notion that a system of instructional variables interact to create optimal conditions for learning.     

Elo-based learner modeling for the adaptive practice of facts

We investigate applications of learner modeling in a computerized adaptive system for practicing factual knowledge. We focus on areas where learners have widely varying degrees of prior knowledge. We propose a modular approach to the development of such adaptive practice systems: dissecting the system design into an estimation of prior knowledge, an estimation of current knowledge, and the construction of questions. We provide a detailed discussion of learner models for both estimation steps, including a novel use of the Elo rating system for learner modeling. We implemented the proposed approach in a system for practising geography facts; the system is widely used and allows us to perform evaluation of all three modules. We compare the predictive accuracy of different learner models, discuss insights gained from learner modeling, as well as the impact different variants of the system have on learners’ engagement and learning.     

Design of adaptive hypermedia learning systems: A cognitive style approach

In the past decade, a number of adaptive hypermedia learning systems have been developed. However, most of these systems tailor presentation content and navigational support solely according to students’ prior knowledge. On the other hand, previous research suggested that cognitive styles significantly affect student learning because they refer to how learners process and organize information. To this end, the study presented in this paper developed an adaptive hypermedia learning system tailored to students’ cognitive styles, with an emphasis on Pask’s Holist–Serialist dimension. How students react to this adaptive hypermedia learning system, including both learning performance and perceptions, was examined in this study. Forty-four undergraduate and postgraduate students participated in the study. The findings indicated that, in general, adapting to cognitive styles improves student learning. The results also showed that the adaptive hypermedia learning system have more effects on students’ perceptions than performance. The implications of these results for the design of adaptive hypermedia learning systems are discussed.     

Activity sequence modelling and dynamic clustering for personalized e-learning

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners?? problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.     

基于Multi-Agent的协作学习系统

针对当前的协作学习系统很少考虑到学习者知识水平、认知特性、兴趣等个性化属性，提出了一个基于学习者知识水平的分层多Agent学习系统模型，该模型结合智能代理技术，通过对学习者知识水平的界定实现系统的分层结构，较好地解决了现有学习系统中普遍存在而又尚未解决的无序和混乱、群体互动效果、个性化以及系统通讯开销等问题。     

Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners’ learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others’ learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.