Publication Date

12-2021

Date of Final Oral Examination (Defense)

12-18-2021

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Computer Science

Department

Computer Science

Major Advisor

Maria Soledad Pera, Ph.D.

Advisor

Jerry Alan Fails, Ph.D.

Advisor

Casey Kennington, Ph.D.

Advisor

Katherine Landau Wright, Ph.D.

Abstract

Bicycle design has not changed for a long time, as they are well-crafted for those that possess the skills to ride, i.e., 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 the balancing of risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, appropriateness, and adequate readability by analyzing the URLs, snippets, and page titles of Web resources retrieved by a given mainstream search engine. 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, e.g., educational alignment, readability, and objectionability, when applied to the design of algorithms that can better support children's information discovery.

DOI

https://doi.org/10.18122/td/1891/boisestate

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