Information Retrieval has become a very active research field in the 21st century. Many from academia and industry present their innovations in the field in a wide variety of conferences and journals. Companies transfer this new knowledge directly to the general public via services such as web search engines in order to improve their information seeking experience.
In parallel, teaching IR is turning into an important aspect of IR generally, not only because it is necessary to impart effective search techniques to make the most of the IR tools available, but also because we must provide a good foundation for those students who will become the driving force of future IR technologies.
There are very few resources for teaching and learning in IR, the major problem which this book is designed to solve. The objective is to provide ideas and practical experience of teaching and learning IR, for those whose job requires them to teach in one form or another, and where delivering IR courses is a major part of their working lives.
In this context of providing a higher profile for teaching and learning as applied to IR, the co-editor of this book, Efthimis Efthimiathis, had maintained a leading role in teaching and learning within the domain of IR for a number of years. This book represents a posthumous example of his efforts in the area, as he passed away in April 2011. This book, his book, is dedicated to his memory.
Reviews / Votes
"This excellent book is a must-read for anyone struggling to teach IR--whether in a library school, an information school, a computer science department, or an informatics department." -
Donald H. Kraft, ACM Computing Reviews, July 2012
Series
Edition
Softcover reprint of the original 1st ed. 2011
Language
Place of publication
Publishing group
Target group
Professional and scholarly
Illustrations
32 s/w Abbildungen
XV, 213 p. 32 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 13 mm
Weight
ISBN-13
978-3-662-50677-6 (9783662506776)
DOI
10.1007/978-3-642-22511-6
Schweitzer Classification
Efthimis N. EfthimiadisEfthimis Efthimiadis obtained a Ph.D in Information Science from City University London in 1992. His research focused on the design of front-end interfaces that improve access to databases, and on the evaluation of information retrieval systems. Further interests included the application of probabilistic techniques to information retrieval and in methods that incorporate user preferences and user interaction in the retrieval techniques. Efthimiadis' research in the area of query expansion was concerned with the evaluation of ranking algorithms and the study of the searching behaviour of endusers. Professor Efthimiadis taught courses on the principles of information retrieval, database design, online search techniques, internet access, introduction to information science, business information and medical informatics.Juan M. Fernández-LunaJuan Manuel Fernández-Luna got his Computer Science degree in 1994 at the University of Granada, Spain. In 2001 he got his PhD at the same institution, working on a thesis in which several retrieval models based on Bayesian networks for Information Retrieval where designed. Currently, his main research area is XML retrieval, although he also is working in collaboration with Juan F. Huete in collaborative IR, recommender systems, learning to rank and heterogeneous data source integration. He has got experience organizing international conferences and workshops, among them the I and II International Workshops on Teaching and Learning of Information Retrieval. He has been co-editor of several journal special issues, highlighting the special Information Retrieval issue on Teaching and Learning of Information Retrieval. He also belongs to the programme committees of the main IR conferences.Juan F. HueteJuan F. Huete is assistant professor at the Department of Computer Science and Artificial Intelligence at the University of Granada. He got his PhD in1995, researching on the uncertainty treatment in Artificial Intelligence under the formalism of Bayesian networks. From 1998, his research interest is Information Retrieval, designing retrieval models based on these graphical models. He is currently also working in the Recommender System field, although other fields like collaborative IR or learning to rank. He has been co-editor of a special Information and Processing Management issue on Bayesian networks and Information Retrieval. He has co-organized several international conferences, as well as workshops. Among these last types of events, the following three could be highlighted: I and II International Workshop on Teaching and Learning of Information Retrieval and the SIGIR'07 Workshop on Information Retrieval and Graphical Models.Andrew MacFarlaneAndrew MacFarlane is a Senior Lecturer in the Department of Information Science at City University, and currently co-directs the Centre of Interactive Systems Research with Prof Stephen Robertson of Microsoft Research Cambridge. He got his PhD Information Science from the same Department under the supervision of Prof Robertson and Dr. J.A. McCann (now at Imperial College London) in 2000. His research interests currently focus on a number of areas including parallel computing for information retrieval, disabilities and Information Retrieval (dyslexia in particular), AI techniques for Information Retrieval and Filtering, and Open Source Software Development. He is the Chair of the BCS Information Retrieval Specialist Group and is a long standing member of that SG.
Introduction.- Fostering Student Engagement in an Online IR Course.- Teaching IR: Curricular Consideration.- Pedagogical Enhancements for Information Retrieval Courses.- Pedagogical Design and Evaluation of Interactive Information Retrieval Learning Environment.- Shifting Contexts: Relating the User, Search and System in Teaching IR.- A Technical Approach to Information Retrieval Pedagogy.- Using Multiple Choice Questions to Assist Learning for Information Retrieval.- Information Retrieval Systems Evaluation: Learning and Teaching Process.- Teaching Web Information Retrieval to Computer Science Students: A Concrete Approach and its Analysis.- Is a relevant piece of information a valid one? Teaching critical evaluation of online information.- Training students to evaluate search engines.- Teaching information retrieval (IR) through problem based learning (PBL).- Educational Resource Development for Information Retrieval in a Digital Libraries Context.
