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

The impact of cognitive styles on perceptual distributed multimedia quality

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2003 John Wiley & Sons, Inc.Multimedia technology has been widely used in web-based instruction, but previous studies have indicated that individual differences, especially cognitive styles, have significant effects on users’ preferences with respect to presentation of multimedia content. However, such research has thus far neglected to examine the effect of cognitive styles on users’ subjective perceptions of multimedia quality. This study aims to examine the relationships among users’ cognitive styles, the multimedia Quality of Service (QoS) delivered by the underlying network, and Quality of Perception (QoP), which encompasses user levels of enjoyment and understanding of the informational content provided by multimedia material. Accordingly, 132 users took part in an experiment in which they were shown multimedia video clips presented with different values of two QoS parameters (frame rate and colour depth). Results show that, whilst the two QoS parameters do not impact user QoP, multimedia content and dynamism levels significantly influence the user understanding and enjoyment component of QoP

Integration of human factors in networked computing

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2008 Elsevie

Cognitive Styles and Adaptive Web-based Learning

Adaptive hypermedia techniques have been widely used in web-based learning programs. Traditionally these programs have focused on adapting to the user’s prior knowledge, but recent research has begun to consider adapting to cognitive style. This study aims to determine whether offering adapted interfaces tailored to the user’s cognitive style would improve their learning performance and perceptions. The findings indicate that adapting interfaces based on cognitive styles cannot facilitate learning, but mismatching interfaces may cause problems for learners. The results also suggest that creating an interface that caters for different cognitive styles and gives a selection of navigational tools might be more beneficial for learners. The implications of these findings for the design of web-based learning programs are discussed

Mining learning preferences in web-based instruction: Holists vs. Serialists

Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99- 2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)

Navigation in hypermedia learning systems: Experts vs. novices

With the advancement of Web technology, hypermedia learning systems are becoming more widespread in educational settings. Hypermedia learning systems present course content with non-sequential formats, so students are required to develop learning paths by themselves. Yet, empirical evidence indicates that not all students can benefit from hypermedia learning. Research into individual differences suggests that prior knowledge has significant effects on student learning in hypermedia systems, with experts and novices showing different preferences to the use of hypermedia learning systems and requiring different levels of navigation support. It is therefore essential to develop a mechanism to help designers understand the needs of experts and novices. To address this issue, this paper presents a framework to illustrate the needs of students with different levels of prior knowledge by analyzing the findings of previous research. The overall aim of this framework is to integrate students’ prior knowledge into the design of hypermedia learning systems. Finally, implications for the design of hypermedia learning systems are discussed

Investigation of the use of navigation tools in web-based learning: A data mining approach

Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs

Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability

Personalization can be addressed by adaptability and adaptivity, which have different advantages and disadvantages. This study investigates how digital library users react to these two techniques. More specifically, we develop a personalized digital library to suit the needs of different cognitive styles based on the findings of our previous work (Frias-Martinez, et al., in press). The personalized digital library includes two versions: adaptive version and adaptable version. The results showed that users not only performed better in the adaptive version, but also they perceived more positively to the adaptive version. In addition, cognitive styles have great effects on users’ responses to adaptability and adaptivity. These results provide guidance for designers to select suitable techniques to develop personalized digital libraries

Digital libraries: What do users want?

This is the post-print version of the Article of the Article. The official published version can be accessed from the link below - Copyright @ 2006 EmeraldPurpose – The purpose of this study is to determine user suggestions for digital libraries' functionality and features. Design/methodology/approach – A survey was conducted as part of this study, in which users' suggestions for digital libraries were solicited, as well as their ranking opinions on a range of suggested digital library features. Findings – The study revealed that, regardless of users' information technology (IT) backgrounds, their expectations of digital libraries' functionality are the same. However, based on users' previous experiences with digital libraries, their requirements with respect to specific features may change. Practical implications – Involving users in digital library design should be an integral step in the process of building a digital library – in addition to the classic roles of evaluation and testing. Originality/value – In previous digital library user studies, users were involved implicitly (e.g. observed) or explicitly (e.g. diary notes). However, they were never asked to suggest digital library features or functionalities, as this was left to usability and domain experts. This study approached digital library design from a new perspective, giving users an opportunity to express their suggestions on future functionality and features of digital libraries. Moreover, in contrast to previous work, this study has explicitly taken into account the IT abilities of those interacting with a digital library

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 applicatio