Project Activities
The research team will code and analyze students' constructed responses generated from prompts to think aloud and self-explain while reading. The data sets were collected in studies carried out in face-to-face contexts as well as automated reading strategy intelligent tutoring systems. Analyses will focus on identifying indicators of coherence-building and establishing their relations to text comprehension, individual differences, and task constraints.
Structured Abstract
Setting
The data sets are from studies conducted in urban and suburban high schools and universities in Arizona and Illinois.
Sample
The data sets include 791 high school students and 1,111 college students from two- and four-year institutions, including students who have been designated as struggling readers through college admissions standards.
The malleable factor of interest is students' coherence-building strategies and processes during reading.
Research design and methods
The research team will analyze data sets collected through multiple prior studies, in which students were asked to respond to prompts, either to think-aloud or self-explain, while reading. The team will examine the moderating effects of individual differences across multiple constructed response tasks and texts. Data analyses will incorporate measures of students' skills and motivation collected during the previously conducted studies, computational linguistic analyses of students' constructed responses, and expert judgments of comprehension strategy use. In addition, researchers will conduct replication analyses across the multiple data sets to examine the reproducibility of the outcomes.
Control condition
Due to the nature of this project, there is no control condition.
Key measures
Key measures include students' constructed responses to think aloud protocols and self-explanation prompts, vocabulary knowledge, reading skills, working memory, prior knowledge, metacognition, and motivation.
Data analytic strategy
The research team will use multiple linear regression models and linear mixed-effects models to explore how comprehension depends on linguistic features of constructed responses while controlling for individual differences. They will also incorporate analyses from dynamic systems theory to understand how readers coordinate the language in their constructed responses with the language in the text. These models allow the team to quantify stability and change in the properties of constructed responses. Finally, the research team will use machine learning techniques to develop algorithms that predict coherence-building processes and comprehension performance.
People and institutions involved
IES program contact(s)
Project contributors
Products and publications
Researchers will provide evidence of how coherence-building supports critical aspects of text comprehension and how individual differences and tasks moderate these processes. They will also produce peer-reviewed publications and presentations.
Project website:
Publications:
Journal Articles
Feller, D. P., Magliano, J., Sabatini, J., O'Reilly, T., & Kopatich, R. D. (2020). Relations between Component Reading Skills, Inferences, and Comprehension Performance in Community College Readers. Discourse Processes, 57(5-6), 473-490, DOI: 10.1080/0163853X.2020.1759175
Magliano, J. P., Higgs, K., Santuzzi, A., Tonks, S. M., O'Reilly, T., Sabatini, J., ... & Parker, C. (2020). Testing the Inference Mediation Hypothesis in a Post-Secondary Context. Contemporary Educational Psychology, 61 101867.
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To answer additional questions about this project or provide feedback, please contact the program officer.
