“Opening the black box: user-log analyses of children’s e-Book reading and associations with word knowledge”

Alumnus Osman Umarji (Ph.D. ’19) is first author on an article in Reading and Writing investigating elementary students’ in-the-moment reading behaviors with an online choose-your-own adventure e-book.

The title of the article is “Opening the black box: user-log analyses of children’s e-Book reading and associations with word knowledge.”

Co-authors are associate project scientist Stephanie Day, doctoral candidate Ying Xu, fifth-year doctoral student Elham Zargar, fourth-year doctoral student Renzhe Yu, and Chancellor’s Professor Carol Connor.

Umarji is Director of Survey Research and Evaluation and a Senior Fellow at the Yaqeen Institute for Islamic Research. While a doctoral student, Umarji studied the dynamic nature of motivation, including the role of values, goal setting, and self-regulation on behavior. He also explored positive psychology, particularly in the development of character strengths and moral beliefs. He specialized in Learning, Teaching, Cognition, and Development and was advised by Distinguished Professor Jacquelynne Eccles.

Xu’s research interests center on technology assisted language learning, digital literacy, and learning analytics. Prior to joining the doctoral program, Xu worked in a digital publishing company, designing educational software for young children. As part of her doctoral studies, she is exploring the potential of conversational agents in promoting young children’s literacy development and science learning. She is specializing in Language, Literacy, and Technology and advised by Professor Mark Warschauer.

Day conducts research that will help improve child outcomes in children with learning difficulties through developing more direct and precise measures of self-regulation and designing interventions aimed at improving these skills in young children. Her research focuses on understanding children’s development in the context of the classroom learning environment, particularly for children with weaker self-regulation and who may be at-risk for developing Attention Deficit Hyperactivity Disorder (ADHD). 

Zargar’s research interests include enhancing and individualizing education using both classroom instruction and technology by applying the science of learning as well as cognitive science principles. The Precision Learning Center (PrecL) awarded Zargar a Catalyst Award to support her research. She is specializing in Learning, Teaching, Cognition, and Development. Her advisor is Associate Professor Susanne Jaeggi.

Yu is specializing in Language, Literacy, and Technology. His research interests include learning analytics, learning sciences, instructional design, and computational modeling. He is focusing on advanced computational methods to model and decipher student learning processes from the rich data available in various digital learning environments, with the goal of improving instructional design based on such empirical evidence. Yu is advised by Professor Mark Warschauer.

Abstract

Third to fifth graders read an interactive choose-your-own adventure e-Book. User logs recorded their reading behaviors and were used to investigate students’ in-the-moment reading behaviors. Reader’s standards of coherence, motivation, and reading strategies were hypothesized to relate to children’s reading behaviors, such as time reading pages, answering embedded questions correctly, and making thoughtful decisions, and in turn to word knowledge gains. Structural equation models revealed that the more time students spent reading embedded questions, the more likely they were to answer the questions correctly, which in turn strongly predicted gains in word knowledge. The more time students spent reading text pages, the more likely they were to make good decisions. Additionally, student participation in a weekly book club, randomly assigned within classrooms, predicted stronger gains in word knowledge. Findings highlight the potential of e-Books to improve word knowledge, and that student user-logs offer another way to study reading comprehension in-the-moment.