2007 Teaching and Learning Symposium

2:30-4:30 p.m., April 24, 2007
HUB West Ballroom

Session Description


IR-Toolbox: An experiential learning tool for teaching Information Retrieval

Efthimis N. Efthimiadis - Information School


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 important than ever before to know "how search works" in order to be more effective in their work. Search Engines or Information Retrieval systems often appear to searchers as "black boxes." There is some sort of magic that happens between typing some keywords in a query box and getting back results. This approach contributes to the development of inadequate conceptual models of search.

The IR-Toolbox is an experiential teaching tool for learning about information retrieval (IR) systems. Through hands on interaction, the IR-Toolbox helps students develop their conceptual model of search engines by exploring, visualizing, and understanding IR processes and algorithms without needing to program. In a sequential fashion, the IR-Toolbox presents the following processing steps:

a) Document analysis (e.g., tokenizers [letter, white-space, grammar], stemmers [Porter, Krovetz], and a variety of stop lists),

b) Indexing (e.g., ability to browse the inverted file and extract statistics),

c) Searching (e.g., ability to enter queries and select weighing algorithms such as IDF, tf-IDF, OKAPI/BM25),

d) Evaluation (e.g., evaluate results using the TREC evaluation software (trec-eval) and associated TREC collections, presenting recall-precision tables and graphs).

Students can interact with the IR-Toolbox 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.

To evaluate the effectiveness of the IR-Toolbox students complete online questionnaires using Catalyst WebQ. Results show that students really like the IR-Toolbox, because they are able to see what happens behind the scenes, and understand how algorithms work. The poster will present examples of how the IR-Toolbox is used, and results from the student surveys.



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