Publication Date

5-2025

Date of Final Oral Examination (Defense)

7-25-2025

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Computer Science

Department

Computer Science

Supervisory Committee Chair

Jerry Fails, Ph.D.

Supervisory Committee Member

Casey Kennington, Ph.D.

Supervisory Committee Member

Maria Soledad Pera, Ph.D.

Supervisory Committee Member

Katherine Wright, Ph.D.

Abstract

As children interact online with search engine result pages (SERPs), children’s comprehension monitoring skills are put to work. Children are known to struggle with utilizing online information as the text content can be misaligned with their reading abilities. Comprehension monitoring skills allow people to assess the comprehensibility of text and is measured by how well comprehension predictions align with performance on comprehension tests. Previous research has shown that augmenting standard SERPs with readability visual cues can be helpful for children searching online. While comprehension predictions are traditionally collected after reading, SERP information provides the opportunity to measure children's perceived comprehensibility of online content before they read. In this work, we explore and analyze the relationship between children's comprehension predictions, SERP information, and SERP navigation. Outcomes reveal that participants tend towards overconfidence in SERP-based predictions, but the utilization of certain SERP information when making predictions results in improved prediction accuracy.

DOI

https://doi.org/10.18122/td.2352.boisestate

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