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Graduate Defense: Garrett Allen
October 19 @ 1:30 pm - 2:30 pm MDT
Title: Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children’s Information Seeking in the Classroom
Program: Master of Science in Computer Science
Advisor: Dr. Sole Pera, Computer Science
Committee Members: Dr. Katherine Wright, Literacy, Language, and Culture, Dr. Casey Kennington, Computer Science, and Dr. Jerry Alan Fails, Computer Science
Bicycle design has not changed for a long time, as they are well-crafted for those that possess the skills to ride, e.g. adults. Those learning to ride, however, often need additional support in the form of training wheels. Searching for information on the Web is much like riding a bicycle, where modern search engines (the bicycle) are optimized for general use and adult users, but lack the functionality to support non-traditional audiences and environments. In this thesis, we introduce a set of training wheels in the form of a learning to rank model as augmentation for standard search engines to support classroom search activities for children ages (6–11). This new model extends the known listwise learning to rank framework through balancing of risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, readability, and appropriateness by analyzing the URLs, search snippets, and page titles of Web resources. Experiments including an ablation study and comparisons with existing baselines showcase the correctness of the proposed model. Outcomes of this work demonstrate the value of considering multiple perspectives inherent to the classroom setting when applied to the design of algorithms that can better support children’s information discovery.