Sherry Y. Chen

Cognitive styles and hypermedia navigation: development of a learning model

There has been an increased growth in the use of hypermedia to deliver learning and teaching material. However, much remains to be learned about how different learners perceive such systems. Therefore, it is essential to build robust learning models to illustrate how hypermedia features are experienced by different learners. Research into individual differences suggests cognitive styles have a significant effect on student learning in hypermedia systems. In particular, Witkins Field Dependence has been extensively examined in previous studies. This article reviews the published findings from empirical studies of hypermedia learning. Specifically, the review classifies the research into five themes: nonlinear learning, learner control, navigation in hyperspace, matching and mismatching, and learning effectiveness. A learning model, developed from an analysis of findings of the previous studies, is presented. Finally, implications for the design of hypermedia learning systems are discussed.

Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application.

Web-based interaction: A review of three important human factors

With the rapid development of information technology, the World Wide Web has been widely used in various applications, such as search engines, online learning and electronic commerce. These applications are used by a diverse population of users with heterogeneous backgrounds, in terms of their knowledge, skills, and needs. Therefore, human factors are key issues for the development of Web-based applications, leading research into human factors to grow significantly in the past decade. This paper identifies and reviews three important human factors that have been examined in existing empirical studies, including gender differences, prior knowledge, and cognitive styles. The main results from the analysis include that: (a) females have more disorientation problems than males; (b) flexible paths are more beneficial to experts while structured content is more useful to novices; and (c) Field Dependent and Field Independent users prefer to employ different search strategies. In addition to reviewing the existing empirical studies, this paper also highlights areas of future research.

Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the application

Editorial: Data mining for understanding user needs

Data mining and data analysis have a long history in human-computer interaction, starting with early interests in tracking the users then trying to infer models of users for adaptive systems [Benyon and Murray 1993; Fischer 1993], to more recent interests in attentional user interfaces, notifier systems, and recommenders. Recommender systems have emerged as a research area meriting a conference series since 2007, while attentional UIs have been the subject of several special issues [Horvitz et al. 2003; McCrickard et al. 2003b]. The convergence of analytic techniques for establishing patterns and orders in large datasets—data mining—and using such analysis to improve the responsiveness, user fit, and functionality of interactive systems has not been explicitly synthesized even though it has been a persistent interest in HCI. This special issue is therefore timely in bringing the fields of data mining and HCI together, As technology has developed over the past few decades, vast amounts of data have been generated as a result of users’ interactions with a range of applications from e-commerce to social networking sites. Analyzing this data can help in understanding the users’ needs and evaluating the effectiveness of user interaction. In turn, this can be used to improve the interface and interaction design, determine more suitable content, and develop useful services targeted at individual users. Data mining, also known as knowledge discovery [Fayyad and Uthurusamy 1996], is the process of extracting valuable information from large amounts

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research.

Advances in Web-Based Education: Personalized Learning Environments.

PART 1: Modelling the learner - Chapter 1: Gender differences and hypermedia navigation: Principles for adaptive hypermedia learning systems Chapter 2: Modelling learners cognitive abilities in the context of a Web-based learning environment Chapter 3: Dominant meanings towards individualized Web search for learning Chapter 4: An adaptive predictive model for student modelling Chapter 5: Giving learners a real sense of control over adaptivity, even if they are not quite ready for it yet PART 2: Designing instruction - Chapter 6: Building an instructional framework to support learner control in adaptive educational hypermedia systems Chapter 7: Bridging the gap with MAID: A method for adaptive instructional design Chapter 8: An adaptive feedback framework to support reflection, guiding and tutoring Chapter 9: Adaptable navigation in a SCORM compliant learning module PART 3: Authoring and exploring the content - Chapter 10: Authoring of adaptive hypermedia Chapter 11: Authoring of adaptive hypermedia courseware using AHyCo system Chapter 12: TEXT - COL- A tool for active reading PART 4: Approaches to integration - Chapter 13: From non-adaptive to adaptive educational hypermedia: Theory, research, and methodological issues Chapter 14: Contextualized learning: Supporting learning in context.

Automated user modeling for personalized digital libraries

Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a users necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information.

Data mining from 1994 to 2004: an application-orientated review

Data mining, which is also known as knowledge discovery, is one of the most popular topics in information technology. It concerns the process of automatically extracting useful information and has the promise of discovering hidden relationships that exist in large databases. These relationships represent valuable knowledge that is crucial for many applications. This paper presents a review of works on current applications of data mining, which focus on four main application areas, including bioinformatics data, information retrieval, adaptive hypermedia and electronic commerce. How data mining can enhance functions for these four areas is described. The reader of this paper is expected to get an overview of the state-of-the-art research associated with these applications. Furthermore, we identify the limitations of current works and raise several directions for future research.

The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception.