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

Understanding Novice Users\u27 Help-seeking Behavior in Getting Started with Digital Libraries: Influence of Learning Styles

Users\u27 information needs have to be fulfilled by providing a well-designed system. However, end users usually encounter various problems when interacting with information retrieval (IR) systems and it is even more so for novice users. The most common problem reported from previous research is that novice users do not know how to get started even though most IR systems contain help mechanisms. There is a deep gap between the system\u27s help function and the user\u27s need. In order to fill the gap and provide a better interacting environment, it is necessary to have a clearer picture of the problem and understand what the novice users\u27 behaviors are in using IR systems. The purpose of this study is to identify novice users\u27 help-seeking behaviors while they get started with digital libraries and how their learning styles lead to these behaviors. While a novice user is engaged in the process of interacting with an IR system, he/she may easily encounter problematic situations and require some kind of help in the search process. Novice users need to learn how to use a new IR environment by interacting with help features to fulfill their searching needs. However, many research studies have demonstrated that the existing help systems in IR systems cannot fully satisfy users\u27 needs. In addition to the system side problems, users\u27 characteristics, such as preference in using help, also play major roles in the decision of using system help. When viewing help-seeking as a learning activity, learning style is an influential factor that would lead to different help-seeking behaviors. Learning style deeply influences how students process information in learning activities, including learning performance, learning strategy, and learning preferences. Existing research does not seem to consider learning style and help-seeking together; therefore, the aim of this study is to explore the effects of learning styles on help-seeking interactions in the information seeking and searching environment. The study took place in an academic setting, and recruited 60 participants representing students from different education levels and disciplines. Data were collected by different methods, including pre-questionnaire, cognitive preference questionnaire, think-aloud protocol, transaction log, and interview. Both qualitative and quantitative approaches were employed to analyze data in the study. Qualitative methods were first applied to explore novice users\u27 help-seeking approaches as well as to illustrate how learning styles lead to these approaches. Quantitative methods were followed to test whether or not learning style would affect help-seeking behaviors and approaches. Results of this study highlight two findings. First, this study identifies eight types of help features used by novice users with different learning styles. The quantitative evidence also verifies the effect of learning styles on help-seeking interactions with help features. Based on the foundation of the analysis of help features, the study further identified fifteen help-seeking approaches applied by users with different learning styles in digital libraries. The broad triangulation approach assumed in this study not only enables the illustration of novice users\u27 diversified help-seeking approaches but also explores and confirms the relationships between different dimensions of learning styles and help-seeking behaviors. The results also suggest that the designs and delivery of IR systems, including digital libraries, need to support different learning styles by offering more engaging processing layouts, diversified input formats, as well as easy-to-perceive and easy-to-understand modes of help features

RSS Feeds, Browsing and End-User Engagement

Despite the vast amount of research that has been devoted separately to the topics of browsing and Really Simple Syndication (RSS) aggregation architecture, little is known about how end-users engage with RSS feeds and how they browse while using a feed aggregate. This study explores the browsing behaviors end-users exhibit when using RSS and Atom feeds. The researcher analyzed end-users’ browsing experiences and discusses browsing variations. The researcher observed, tested, and interviewed eighteen (N=18) undergraduate students at the University of Tennessee to determine how end-users engage with RSS feeds. This study evaluates browsing using two variations of tasks, (1) an implicit task with no final goal and (2) an explicit task with a final goal. The researcher observed the participants complete the two tasks and conducted exit interviews, which addressed the end-users’ experiences with Google Reader and provided further explanation of browsing behaviors. The researcher analyzed the browsing behaviors based upon Bates’ (2007) definitions and characteristics of browsing. The results of this exploratory research provide insights into end-user interaction with RSS feeds

Encountering on the road to Serendip? Browsing in new information environments

Considers the continuing relevance of the ideas of browsing, serendipity, information encountering, and literature discovery in a digital information environment

Youth and Digital Media: From Credibility to Information Quality

Building upon a process-and context-oriented information quality framework, this paper seeks to map and explore what we know about the ways in which young users of age 18 and under search for information online, how they evaluate information, and how their related practices of content creation, levels of new literacies, general digital media usage, and social patterns affect these activities. A review of selected literature at the intersection of digital media, youth, and information quality -- primarily works from library and information science, sociology, education, and selected ethnographic studies -- reveals patterns in youth's information-seeking behavior, but also highlights the importance of contextual and demographic factors both for search and evaluation. Looking at the phenomenon from an information-learning and educational perspective, the literature shows that youth develop competencies for personal goals that sometimes do not transfer to school, and are sometimes not appropriate for school. Thus far, educational initiatives to educate youth about search, evaluation, or creation have depended greatly on the local circumstances for their success or failure

WEBSITE GLOBALIZATION STRATEGY : A CROSS-CULTURAL ANALYSIS OF WEBSITE STRUCTURE

Master'sMASTER OF SCIENC

Multi Layer Feed Forward Artificial Neural Network For Learning Styles Identification

Accommodating learning styles in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning process as well as teacher and learner satisfaction. The premise is that a fact that learning in the classroom is less efficient, when teachers will not be able to get insight of each of the student’s learning style; hence, they won't be able to adapt their teaching strategies to match with the student’s learning style. In order to get an insight of the student’s learning style in AEHS, the system must be able to recognize the learning styles of the students. Current methods for recognizing learning styles are less efficient, where questionnaires will lead to tedium and disturbance at learning processes. Thus, this study developed the learning styles based AEHS that utilized Multi Layer Feed-Forward Artificial Neural Network (MLFF) which was used to identify student’s learning styles in real-time. The automatic and real-time learning styles identification was done by analyzing the student’s browsing behavior while they are learning through the proposed AEHS. The system then adaptively presents the learning content that matches with the students’ learning styles by the means of fragment sorting and adaptive annotation technique. At the end of the study, the data triangulation was done to test if incorporating learning styles in learning environments can impact the student achievement. It was done by asking the student to answer the mini quiz after they were using the proposed AEHS with adaptive feature was activated. This study also focused on analysis of the existence of the relationship between the frequencies of students’ click on learning components with their staying time on those particular learning components. The result showed that the proposed MLFF performed well in identifying the students’ learning styles in real-time. Moreover, the analyzed student’s browsing behavior revealed that there was a relationship between the frequencies of the students’ click on learning components with their staying time on those particular components. Furthermore, after the student’s learnt through the proposed AEHS with adaptive feature activated and answered the mini quiz result; most of them could achieve the perfect score. In this case, the mini quiz result showed that incorporating learning styles into learning environment may affect and increase student’s achievements

A new integrated model for multitasking during web searching

Investigating multitasking information behaviour, particularly while using the web, has become an increasingly important research area. People s reliance on the web to seek and find information has encouraged a number of researchers to investigate the characteristics of information seeking behaviour and the web seeking strategies used. The current research set out to explore multitasking information behaviour while using the web in relation to people s personal characteristics, working memory, and flow (a state where people feel in control and immersed in the task). Also investigated were the effects of pre-determined knowledge about search tasks and the artefact characteristics. In addition, the study also investigated cognitive states (interactions between the user and the system) and cognitive coordination shifts (the way people change their actions to search effectively) while multitasking on the web. The research was exploratory using a mixed method approach. Thirty University students participated; 10 psychologists, 10 accountants and 10 mechanical engineers. The data collection tools used were: pre and post questionnaires, pre-interviews, a working memory test, a flow state scale test, audio-visual data, web search logs, think aloud data, observation, and the critical decision method. Based on the working memory test, the participants were divided into two groups, those with high scores and those with lower scores. Similarly, participants were divided into two groups based on their flow state scale tests. All participants searched information on the web for four topics: two for which they had prior knowledge and two more without prior knowledge. The results revealed that working memory capacity affects multitasking information behaviour during web searching. For example, the participants in the high working memory group and high flow group had a significantly greater number of cognitive coordination and state shifts than the low working memory group and low flow group. Further, the perception of task complexity was related to working memory capacity; those with low memory capacity thought task complexity increased towards the end of tasks for which they had no prior knowledge compared to tasks for which they had prior knowledge. The results also showed that all participants, regardless of their working memory capacity and flow level, had the same the first frequent cognitive coordination and cognitive state sequences: from strategy to topic. In respect of disciplinary differences, accountants rated task complexity at the end of the web seeking procedure to be statistically less significant for information tasks with prior knowledge compared to the participants from the other disciplines. Moreover, multitasking information behaviour characteristics such as the number of queries, web search sessions and opened tabs/windows during searches has been affected by the disciplines. The findings of the research enabled an exploratory integrated model to be created, which illustrates the nature of multitasking information behaviour when using the web. One other contribution of this research was to develop new more specific and closely grounded definitions of task complexity and artefact characteristics). This new research may influence the creation of more effective web search systems by placing more emphasis on our understanding of the complex cognitive mechanisms of multitasking information behaviour when using the web

Challenges to Teaching Credibility Assessment in Contemporary Schooling

Part of the Volume on Digital Media, Youth, and CredibilityThis chapter explores several challenges that exist to teaching credibility assessment in the school environment. Challenges range from institutional barriers such as government regulation and school policies and procedures to dynamic challenges related to young people's cognitive development and the consequent difficulties of navigating a complex web environment. The chapter includes a critique of current practices for teaching kids credibility assessment and highlights some best practices for credibility education

Understanding Children’s Help-Seeking Behaviors: Effects of Domain Knowledge

This dissertation explores children’s help-seeking behaviors and use of help features when they formulate search queries and evaluate search results in IR systems. This study was conducted with 30 children who were 8 to 10 years old. The study was designed to answer three research questions with two parts in each: 1(a) What are the types of help-seeking situations experienced by children (8-10 years old) when they formulate search queries in a search engine and a kid-friendly web portal?, 1(b) What are the types of help-seeking situations experienced by children (8-10 years old) when they evaluate search results in a search engine and a kid-friendly web portal?, 2(a) What types of help features do children (8-10 years old) use and desire when they formulate search queries in a search engine and a kid-friendly web portal?, 2(b) What types of help features do children (8-10 years old) use and desire when they evaluate search results in a search engine and a kid-friendly web portal?, 3(a) How does children’s (8-10 years old) domain knowledge affect their help seeking and use of help features when they formulate search queries in a search engine and a kid-friendly web portal?, 3(b) How does children’s (8-10 years old) domain knowledge affect their help seeking and use of help features when they evaluate search results in a search engine and a kid-friendly web portal? This study used multiple data collection methods including performance-based domain knowledge quizzes as direct measurement, domain knowledge self-assessments as indirect measurement, pre-questionnaires, transaction logs, think-aloud protocols, observations, and post-interviews. Open coding analysis was used to examine children’s help-seeking situations. Children’s cognitive, physical, and emotional types of help-seeking situations when using Google and Kids.gov were identified. To explore help features children use and desire when they formulate search queries and evaluate results in Google and Kids.gov, open coding analysis was conducted. Additional descriptive statistics summarized the frequency of help features children used when they formulated search queries and evaluated results in Google and Kids.gov. Finally, this study investigated the effect of children’s domain knowledge on their help seeking and use of help features in using Google and Kids.gov based on linear regression. The level of children’s self-assessed domain knowledge affects occurrences of their help-seeking situations when they formulated search queries in Google. Similarly, children’s domain knowledge quiz scores showed a statistically significant effect on occurrences of their help-seeking situations when they formulated keywords in Google. In the stage of result evaluations, the level of children’s self-assessed domain knowledge influenced their use of help features in Kids.gov. Furthermore, scores of children’s domain knowledge quiz affected their use of help features when they evaluated search results in Kids.gov. Theoretical and practical implications for reducing children’s cognitive, physical, and emotional help-seeking situations when they formulate search queries and evaluate search results in IR systems were discussed based on the results