Progressor: social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.     

Analysis of learners' navigational behaviour and their learning styles in an online course

Providing adaptive features and personalized support by considering students' learning styles in computer‐assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour of students in an online course within a learning management system was investigated, looking at how students with different learning styles prefer to use and learn in such a course. As a result, several differences in the students' navigation patterns were identified. These findings have several implications for improving adaptivity. First, they showed that students with different learning styles use different strategies to learn and navigate through the course, which can be seen as another argument for providing adaptivity. Second, the findings provided information for extending the adaptive functionality in typical learning management systems. Third, the information about differences in navigational behaviour can contribute towards automatic detection of learning styles, helping in making student modeling approaches more accurate.     

COMBINING MULTI‐AGENT PARADIGM AND MEMETIC COMPUTING FOR PERSONALIZED AND ADAPTIVE LEARNING EXPERIENCES

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Computational Intelligence methodologies can support e‐Learning system designers in two different aspects: (1) they represent the most suitable solution able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop “in time” e‐Learning environments. This article attempts to achieve both results by exploiting an ontological representations of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework. This synergy enables multi‐island memetic approach managing a collection of models and processes for adapting an e‐Learning system to the learner expectations and to formulate objectives in an effective and dynamic intelligent way. More precisely, our proposal exploits ontological representations of learning environment and a memetic distributed problem‐solving approach to generate the best learning presentation and, at the same time, minimize the computational efforts necessary to compute optimal learning experiences.     

Adaptive assessment of student's knowledge in programming courses

This paper presents Programming Adaptive Testing (PAT), a Web‐based adaptive testing system for assessing students' programming knowledge. PAT was used in two high school programming classes by 73 students. The question bank of PAT is composed of 443 questions. A question is classified in one out of three difficulty levels. In PAT, the levels of difficulties are adapted to Bloom's taxonomy lower levels, and students are examined in their cognitive domain. This means that PAT has been designed according to pedagogical theories in order to be appropriate for the needs of the course ‘Application Development in a Programming Environment’. If a student answers a question correctly, a harder question is presented, otherwise an easier one. Easy questions examine the student's knowledge, while difficult questions examine the student's skills to apply prior knowledge to new problems. A student answers a personalized test composed of 30 questions. PAT classifies a student in one out of three programming skills' levels. It can predict the corresponding classification of students in Greek National Exams. Furthermore, it can be helpful to both students and teachers. A student could discover his or her programming shortcomings. Similarly, a teacher could objectively assess his or her students as well as discover the subjects that need to be repeated.     

User-centered indexing for adaptive information access

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.Dr. Mathé and Dr. Chen are contractors with Recom Technologies, Inc.     

AH-questionnaire: An adaptive hierarchical questionnaire for learning styles

One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the general tendency of the student and not the specific score obtained in each dimension. In this context, we present AH-questionnaire, a new approach to minimize the number of questions needed to classify student Learning Styles. Based on the Felder-Silverman’s Learning Style Model, it aims at classifying students into categories in spite of providing precise scores. The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning style preference with high accuracy and only a few questions.     

SN‐Learning: An exploratory study beyond e‐learning and evaluation of its applications using EV‐SNL framework

Social networks have intruded in human life by providing new technological innovations in a range of fields, including the education. The use of social networks in education has the potential to extend e‐learning and to introduce new forms of tutoring, communication, and collaboration between students and instructors. Thus, e‐learning is the threshold of Social Networking‐based Learning (SN‐Learning). SN‐Learning consists of a new term, introduced in this paper, and involves e‐learning systems with social networking characteristics or learning through social networking platforms. To this direction, the main objective of this paper is to present this new technological advancement emerged nowadays and to evaluate relevant applications of the last decade using our adjusted evaluation framework, EV‐SNL, in order to highlight their strengths and weaknesses regarding digital learning. The major finding is that SN‐Learning systems focus mainly on the incorporation of social features and do not provide yet personalized and adaptive tutoring. This research provides guidelines to computer science researchers on the design and implementation of SN‐Learning platforms using artificial intelligence and modelling techniques. Moreover, it can support teachers of different fields so that they can enhance their instruction with new technologies. There is scope for a lot of improvement.     

基于学习风格的自适应式虚拟交互方法的研究

新工科教育的改革推进了自适应学习的发展,以往的自适应方式对学习者的主观能动性的重视不足,且大部分已有研究无法提供个性化程度较高的交互体验.针对这些问题,该研究基于学习风格模型提出了一种自适应式虚拟现实交互的新方法.该方法设计了适用于虚拟交互环境的学习风格判断方式,通过分析学习者的主客观数据来判断其学习风格,由此对交互环...     

An adaptive navigation support system for conducting context-aware ubiquitous learning in museums

In context-aware ubiquitous learning, students are guided to learn in the real world with personalized supports from the learning system. As the learning resources are realistic objects in the real world, certain physical constraints, such as the limitation of stream of people who visit the same learning object, the time for moving from one object to another, and the environmental parameters, need to be taken into account. Moreover, the values of these context-dependent parameters are likely to change swiftly during the learning process, which makes it a challenging and important issue to find a navigation support mechanism for suggesting learning paths for individual students in real time. In this paper, the navigation support problem for context-aware ubiquitous learning is formulated and two navigation support algorithms are proposed by taking learning efficacy and navigation efficiency into consideration. From the simulation results of learning in a butterfly museum setting, it is concluded that the innovative approach is helpful to the students to more effectively and efficiently utilize the learning resources and achieve better learning efficacy.     

自适应网络学习用户界面设计和实现

An adaptive user interface helps to improve the quality of human-computer interaction. Most people at present join to Web-Based Learning by common browser. Due to the one-fits-all user interface, they have to face with the problem of lack of the support on personalized learning. The design and implementation of the adaptive user interface for Web-based learning in this paper is grounded in our work done before, for example interaction model,adaptive user models including domain models. The adaptivity is mainly expressed on learning contents and representation including layout as well as operation.     

Moving beyond the direct impact of using CRM systems on frontline employees' service performance: The mediating role of adaptive behaviour

Despite substantial investments in customer‐relationship‐management (CRM) systems, companies continue to experience pain rather than profit. Meanwhile, the concept of “adaptive behaviour” of frontline employees has received little attention in the literature related to CRM systems in which the frontline employees are the primary users. In this study, we propose that with the aid of CRM systems, individual employees are able to immediately access information about customers and service offerings, thus enabling their adaptive behaviours to provide personalized service to individual customers. Based on coping theory, we develop a CRM system‐driven adaptive behaviour model that explains how CRM systems facilitate individual employees' service performance by enabling adaptive behaviour during their service encounters. Multisourced data from a financial company in China largely support our proposed model, showing that employees' postadoption of CRM systems (routinization and infusion of use) enables interpersonal and offering adaptive behaviours, which in turn enhance employees' service performance. In addition, forming a postadoption behaviour of CRM systems relies on the frequent use. We discuss the theoretical and practical implications of adaptive behaviour in service encounters with the aid of CRM systems.     

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point‐wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point‐wise learning to rank problem. The prediction performance is analyzed in comparison to three well‐known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.     

A knowledge‐structure‐based adaptive dynamic assessment system for calculus learning

The purpose of this study was to investigate the effect of a calculus system that was designed using an adaptive dynamic assessment (DA) framework on performance in the “finding an area using an integral”. In this study, adaptive testing and dynamic assessment were combined to provide different test items depending on students’ abilities. Prompts were provided from among various options. Two hundred fifty‐seven freshmen from one public university in Taiwan participated in the study. The pre‐test was held within one week after the examinees learned how to find an area using an integral. Remedial instruction was completed within two weeks, and the students were administered the post‐ test. Two weeks after the administration of the post‐test, a delayed post‐test was administered to evaluate the students’ retention. When an examinee responded to an item correctly, he received a score. Otherwise, the student was given a prompt. In this study, five experimental groups were compared: three different DA groups, one self‐study group and one remedial group instruction. The results of the study revealed that the instructive effect of the adaptive dynamic assessment approach (the third DA group) was the best and that the proposed methods helped students improve their learning performance.     

Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into account

In a context-aware ubiquitous learning environment, learning systems are aware of students’ locations and learning status in the real world via the use of sensing technologies which provide personalized guidance or support. In such a learning environment that guides students to observe and learn from real-world targets, various physical world constraints need to be taken into account when planning learning paths for individuals. In this study, an optimization problem is formulated by taking the relevance of real-world learning targets and the environmental constraints into account when determining personalized learning paths in the real world to maximize students’ learning efficacy. Moreover, a hyper-heuristic approach is proposed to efficiently find quality learning paths for individual students. To evaluate the performance of the proposed approach, the teachers’ feedback was collected and analyzed based on the learning activities conducted in an elementary school natural science course; in addition, the performances of the proposed algorithm and other approaches were compared based on a set of test data.     

面向在线智慧学习的教育数据挖掘技术研究

随着教育信息化进程的深入,学生在线学习数据得到不断积累,为数据驱动的教育评估和智能辅助教学提供良好条件.然而,已有的面向在线智慧学习的教育数据挖掘模型很难从海量、稀疏、高噪的数据中准确分析试题特征和学生学业水平,也较少考虑学生及教师的个性化需求.文中针对上述问题开展若干面向在线智慧学习的教育数据挖掘技术研究工作,以教育学习所涉及的试题、学生、教师为对象,以个性化推荐等技术同教育领域知识相结合为手段,以提高学生学业水平为目标.具体介绍用于试题分析和检索的试题文本表征模型、基于认知诊断的个性化学习资源推荐方法、针对教师的教学建议和指导等方法,以及这些技术所依托的应用平台——科大讯飞在线教育系统“智学网”.最后简单讨论面向在线智慧学习的教育数据挖掘技术未来可能的研究方向.     

Micro‐adaptivity: protecting immersion in didactically adaptive digital educational games

The idea of utilizing the rich potential of today's computer games for educational purposes excites educators, scientists and technicians. Despite the significant hype over digital game‐based learning, the genre is currently at an early stage. One of the most significant challenges for research and development in this area is establishing intelligent mechanisms to support and guide the learner, and to realize a subtle balance between learning and gaming, and between challenge and ability on an individual basis. In contrast to traditional approaches of adaptive and intelligent tutoring, the key advantage of games is their immersive and motivational potential. Because of this, the psycho‐pedagogical and didactic measures must not compromise gaming experience, immersion and flow. In the present paper, we introduce the concept of micro‐adaptivity, an approach that enables an educational game to intelligently monitor and interpret the learner's behaviour in the game's virtual world in a non‐invasive manner. On this basis, micro‐adaptivity enables interventions, support, guidance or feedback in a meaningful, personalized way that is embedded in the game's flow. The presented approach was developed in the context of the European Enhanced Learning Experience and Knowledge TRAnsfer project. This project also realized a prototype game, demonstrating the capabilities, strengths and weaknesses of micro‐adaptivity.     

COPERNICUS: Context‐Preserving Engine for Route Navigation with Interactive User‐modifiable Scaling

In this paper, we present an automated system for generating context‐preserving route maps that depict navigation routes as a path between nodes and edges inside a topographic network. Our application identifies relevant context information to support navigation and orientation, and generates customizable route maps according to design principles that communicate all relevant context information clearly visible on one single page. Interactive scaling allows seamless transition between the original undistorted map and our new map design, and supports user‐specified scaling of regions of interest to create personalized driving directions according to the drivers needs.     

Hypadapter: An adaptive hypertext system for exploratory learning and programming

We have developed an adaptive hypertext system designed to individually support exploratory learning and programming activities in the domain of Common Lisp. Endowed with domain-specific knowledge represented in a hyperspace of topics, the system builds up a detailed model of the user's expertise which it utilizes to provide personalized assistance. Unlike other work emerging in the field of adaptive hypertext systems, our approach exploits domain and user modelling techniques to support individuals in different ways. The system not only generates individualized presentations of topic nodes, but also provides adaptive navigational assistance for link-based browsing. By identifying and suggesting useful hyperlinks according to the user's knowledge state and preferences, the system encourages and guides exploration. While browsing through the hyperspace of topics, the system analyses the user's navigational behaviour to infer the user's learning progress and to dynamically adapt presentations of topics and links accordingly.     

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.