SIGIR 2007 Proceedings

Demonstration

IR-Toolbox: an experiential learning tool for teaching IR
Efthimis N. Efthimiadis and Nathan G. Freier efthimis@u.washington.edu, nfreier@u.washington.edu
Information School University of Washington Seattle, WA, USA

2. THE SYSTEM
The IR-Toolb ox[2] is an exp eriential teaching tool for learning the processes of information retrieval systems. Through hands on interaction, the IR-Toolb ox helps students develop their conceptual model of search engines by exploring, visualizing, and understanding IR processes and algorithms without needing to program. In a iterative fashion, the IRToolb ox presents the following processing steps: 1. Document analysis (e.g., tokenizers [letter, white-space, grammar], stemmers [Porter, Krovetz], and a variety of stop lists), 2. Indexing (e.g., ability to browse the inverted file and extract statistics), 3. Searching (e.g., ability to enter queries and select weighing algorithms such as IDF, TF-IDF, OKAPI/BM25), 4. Evaluation (e.g., evaluate results using the TREC evaluation software (trec-eval) and associated TREC collections, presenting recall-precision tables and graphs). The IR-Toolb ox uses Lucene as its underlining search engine. Lucene is an op en-source, full-text search engine library written in Java and is supp orted by the Apache Software Foundation[1]. Students can interact with the IRToolb ox at different levels of complexity on individual or group exercises that help them understand the different IR processes and build a more detailed conceptual model of search engines. Though the IR-Toolb ox is designed for the non-technical students, those students with programming interests can access Lucene's code and get a more technical view of the system.

Categories and Subject Descriptors
H.3 [INFORMATION STORAGE AND RETRIEVAL]: ; H.3.1 [Content Analysis and Indexing (Indexing methods; Linguistic processing)]: ; H.3.3 [Information Search and Retrieval (Query formulation; Relevance feedback; Retrieval models; Search process)]: ; I.7.2 [Document Preparation (Index generation)]: ; K.3 [COMPUTERS AND EDUCATION]: ; K.3.1 [Computer Uses in Education ( Computerassisted instruction (CAI); Distance learning)]: ; K.3.2 [Computer and Information Science Education]:

General Terms
Human Factors

Keywords
Exp eriential learning, teaching information retrieval to Library and Information Science students

1.

INTRODUCTION

The explosion of the web has made search an integral part of our daily lives. We search for almost any conceivable topic. Web search engines have made search easily approachable to almost everyone. Yet, for information professionals it is more imp ortant than ever b efore to know "how search works" in order to b e more effective in their work. Search Engines or Information Retrieval (IR) Systems often app ear to searchers as "black b oxes." There is some sort of magic that happ ens b etween typing some keywords in a query b ox and getting back results. This approach contributes to the development of inadequate conceptual models of search.
Copyright is held by the author/owner(s). SIGIR'07, July 23­27, 2007, Amsterdam, The Netherlands. ACM 978-1-59593-597-7/07/0007.

3. ACKNOWLEDGMENTS
The following Information School students have contributed to the success of the IR-Toolb ox: Chong Ki Tsang (MLIS), Andrew Szydlowski (MLIS), Andy Walden (MSIM).

4. REFERENCES
[1] The apache lucene pro ject. available at http://lucene.apache.org. [2] IR-Toolbox. available at http://irtoolb ox.ischool.washington.edu.

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