  We introduce a novel re-ranking model that aims to augment the functionality
of standard search engines to support classroom search activities for children
(ages 6 to 11). This model extends the known listwise learning-to-rank
framework by balancing 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. Experimental results,
including an ablation study and comparisons with existing baselines, showcase
the correctness of the proposed model. The 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.
