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

Automated user modeling for personalized digital libraries

Digital libraries (DL) 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 digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries 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 digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Rule-based User Characteristics Acquisition from Logs with Semantics for Personalized Web-Based Systems

Personalization of web-based information systems based on specialized user models has become more important in order to preserve the effectiveness of their use as the amount of available content increases. We describe a user modeling approach based on automated acquisition of user behaviour and its successive rule-based evaluation and transformation into an ontological user model. We stress reusability and flexibility by introducing a novel approach to logging, which preserves the semantics of logged events. The successive analysis is driven by specialized rules, which map usage patterns to knowledge about users, stored in an ontology-based user model. We evaluate our approach via a case study using an enhanced faceted browser, which provides personalized navigation support and recommendation

Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching

This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students’ learning behaviors in an online business course in Taiwan. The study scientifically identified students’ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers