Project Activities
Structured Abstract
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Research design and methods
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Data analytic strategy
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ERIC Citations: Find available citations in ERIC for this award here and here.
Project Website: https://adulted.autotutor.org/
Additional Online Resources and Information:
- https://adulted.autotutor.org: Acts as the research hub for SARA and AutoTutor-ARC (R305A200413). It delivers information about the background behind SARA, AutoTutor-ARC, the team, and other resources.
- https://captivoice.com: Access for programs and educators to gain access to the SARA assessment.
Select Publications
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.
Hollander, J., Sabatini, J., & Graesser, A. (2022). How item and learner characteristics matter in intelligent tutoring systems data. In International Conference on Artificial Intelligence in Education (pp. 520-523). Springer, Cham
Hollander, J., Sabatini, J., & Graesser, A. C. (2021). An intelligent tutoring system for improving adult literacy skills in digital environments. COABE Journal, 10(2), 59-64.
Hollander, J., Sabatini, J., Graesser, A., Greenberg, D., O'Reilly, T., & Frijters, J. (2023). Importance of learner characteristics in intelligent tutoring for adult literacy. Discourse Processes, 1-13.
Kaldes, G., Higgs, K., Lampi, J., Santuzzi, A., Tonks, S. M., O'Reilly, T., Sabatini, J. P., & Magliano, J. P. (2024). Testing the model of a proficient academic reader (PAR) in a postsecondary context. Reading and Writing, 1-40.
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.
Magliano, J. P., Talwar, A., Feller, D. P., Wang, Z., O'Reilly, T., & Sabatini, J. (2022). Exploring thresholds in the foundational skills for reading and comprehension outcomes in the context of postsecondary readers. Journal of Learning Disorders. 00222194221087387
O'Reilly, T., Sabatini, J., & Wang, Z. (2018). Using Scenario-Based Assessments to Measure Deep Learning. In K. Millis, D. Long, J. Magliano, & K. Weimer (Eds.), Deep learning: Multi-disciplinary approaches (pp. 197-208). New York, NY: Routledge.
Sabatini, J, O'Reilly, T., Dreier, K. & Wang, Z. (2019). Cognitive processing deficits associated with low literacy: Differences between adult- and child-focused models. In D. Perin (Ed), The Wiley Handbook of Adult Literacy (pp. 15-39). Hoboken, NJ: John Wiley & Sons.
Sabatini, J., Graesser, A., Hollander, J., & O'Reilly, T. (2023). A framework of literacy development and how AI can transform theory and practice. British Journal of Educational Technology, 54(5), 1174-1203.
Smith, E. H., Hollander, J., Graesser, A. C., Sabatini, J., & Hu, X. (2021). Integrating SARA assessment with reading comprehension training in AutoTutor. English Teaching, 76(1), 17-29.
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Co-Principal Investigator: O'Reilly, Tenaha
- Education technology developers can integrate assessments into instructional technologies to improve engagement and learning. For example, the SARA assessment, which provides a diagnostic profile for individuals' reading strengths and weaknesses, can be used to guide the selection or measure progress of reading comprehension lessons in classroom or adaptive learning systems such as the AutoTutor-ARC, a digital technology that both adapts to learners' performances and engages them in an immersive learning experience using a three-way conversation with computer agents acting as a tutor and a peer to discuss texts (Hollander et al., 2023; Smith et al. 2021).
- Foundational reading skills are also a substantial source of variability in postsecondary students' ability to perform academic reading tasks as is their ability to construct inferences (bridging and elaborative) while reading, with their ability to construct bridging inferences predicting their performance on measures of close comprehension and their ability to construct elaborative inferences predicting performance on the scenario-based assessment (Feller et al., 2020).
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