Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

A semi-supervised approach to visualizing and manipulating overlapping communities

When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE

Adaptive visualization of research communities

Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects

Using Markov Chains for link prediction in adaptive web sites

The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site

Knowledgezoom for java: A concept-based exam study tool with a zoomable open student model

This paper presents our attempt to develop a personalized exam preparation tool for Java/OOP classes based on a fine-grained concept model of Java knowledge. Our goal was to explore two most popular student model-based approaches: open student modeling and problem sequencing. The result of our work is a Java exam preparation tool, Knowledge Zoom. The tool combines an open concept-level student model component, Knowledge Explorer and a concept-based sequencing component, Knowledge Maximizer into a single interface. This paper presents both components of Knowledge Zoom, reports results of its evaluation, and discusses lessons learned. © 2013 IEEE

A Web-based Adaptive and Intelligent Tutor by Expert Systems

Todays, Intelligent and web-based E-learning is one of regarded topics. So researchers are trying to optimize and expand its application in the field of education. The aim of this paper is developing of E-learning software which is customizable, dynamic, intelligent and adaptive with Pedagogy view for learners in intelligent schools. This system is an integration of adaptive web-based E-learning with expert systems as well. Learning process in this system is as follows. First intelligent tutor determines learning style and characteristics of learner by a questionnaire and then makes his model. After that the expert system simulator plans a pre-test and then calculates his score. If the learner gets the required score, the concept will be trained. Finally the learner will be evaluated by a post-test. The proposed system can improves the education efficiency highly as well as de-creases the costs and problems of an expert tutor. As a result, every time and eve-rywhere (ETEW) learning would be provided via web in this system. Moreover the learners can enjoy a cheap remote learning even at home in a virtual simulated physical class. So they can learn thousands courses very simple and fast.Comment: 10 pages, 3 figures, The Second International Conference on Advances in Computing and Information Technology (ACITY 2012). arXiv admin note: substantial text overlap with arXiv:1304.404

QuizMap: Open social student modeling and adaptive navigation support with TreeMaps

In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg

The Impact of Link Suggestions on User Navigation and User Perception

The study reported in this paper explores the effects of providing web users with link suggestions that are relevant to their tasks. Results indicate that link suggestions were positively received. Furthermore, users perceived sites with link suggestions as more usable and themselves as less disoriented. The average task execution time was significantly lower than in the control condition and users appeared to navigate in a more structured manner. Unexpectedly, men took more advantage from link suggestions than women

Dynamic Content Discovery, Harvesting and Delivery, from Open Corpus Sources, for Adaptive Systems

Abstract. Personalised elearning is being heralded as one of the grand challenges of next generation learning systems, in particular, its ability to support greater effectiveness, efficiency and student empowerment. However, a key problem with such systems is their reliance on bespoke content developed for, and only used by, these systems. The challenge for adaptive systems in scalably supporting personalised elearning is its ability to source, harvest and deliver open corpus content to adaptive content services and personalised elearning systems. This paper examines the issues involved in implementing such an adaptive content service. The paper seeks to explore the accurate extraction of content requirements from the adaptive system, the sourcing and identification of suitable learning content, the harvesting and customisation of the content for delivery to adaptive elearning systems.