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Conrad Borchers; Ha Tien Nguyen; Paulo F. Carvalho; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Parent and caregiver involvement in homework can be critical to student success, yet many families face barriers such as limited time and content knowledge. We examine two approaches to involving caregivers in homework that students complete with tutoring systems for middle school mathematics. The first, an intelligent caregiver support module,…
Descriptors: Parent Role, Caregiver Role, Homework, Middle School Students
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Conrad Borchers; Jeroen Ooge; Cindy Peng; Vincent Aleven – Grantee Submission, 2025
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Learner Controlled Instruction, Learning Analytics
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Sam Ford; Mohamed Allali – International Journal of Mathematical Education in Science and Technology, 2023
Studies across a variety of educational fields have shown the efficacy of feedback on student performance and learning. Web-based homework is a common feature of secondary and collegiate mathematics courses to provide such feedback. While web-based homework provides often instantaneous feedback to students as they complete assignments, the…
Descriptors: Intelligent Tutoring Systems, Feedback (Response), Equations (Mathematics), Homework
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Devika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics
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Ha Tien Nguyen; Conrad Borchers; Meng Xia; Vincent Aleven – Grantee Submission, 2024
Intelligent tutoring systems (ITS) can help students learn successfully, yet little work has explored the role of caregivers in shaping that success. Past interventions to support caregivers in supporting their child's homework have been largely disjunct from educational technology. The paper presents prototyping design research with nine middle…
Descriptors: Middle School Mathematics, Intelligent Tutoring Systems, Caregivers, Caregiver Attitudes
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Conrad Borchers; Ha Tien Nguyen; Paulo F. Carvalho; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Caregiver involvement in homework is a key contributor to student success, yet its interaction with intelligent tutoring systems remains underexamined. To address this gap, we conducted a technology probe study exploring how a conversational support tool might enhance caregiver assistance during remote math homework within tutoring systems.…
Descriptors: Parent Participation, Parent Role, Homework, Intelligent Tutoring Systems
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King, Emily C.; Benson, Max; Raysor, Sandra; Holme, Thomas A.; Sewall, Jonathan; Koedinger, Kenneth R.; Aleven, Vincent; Yaron, David J. – Journal of Chemical Education, 2022
This report showcases a new type of online homework system that provides students with a free-form interface and dynamic feedback. The ORCCA Tutor (Open-Response Chemistry Cognitive Assistance Tutor) is a production rules-based online tutoring system utilizing the Cognitive Tutoring Authoring Tools (CTAT) developed by Carnegie Mellon University.…
Descriptors: Intelligent Tutoring Systems, Chemistry, Homework, Feedback (Response)
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Cindy Peng; Conrad Borchers; Vincent Aleven – Grantee Submission, 2024
Prior studies identified effective, but mainly non-digital, homework aids. This research involved 18 middle school students in a lo-fi prototyping study to integrate traditional homework support tools with intelligent tutoring systems (ITS), leveraging rich log data for personalized learning. Feature investigations in standardized diaries, goal…
Descriptors: Middle School Students, Intelligent Tutoring Systems, Homework, Design
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Shi Pu; Yu Yan; Brandon Zhang – Journal of Educational Data Mining, 2024
We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…
Descriptors: Prediction, Success, Data Analysis, Learning Analytics
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Zhang, Mengxue; Wang, Zichao; Baraniuk, Richard; Lan, Andrew – International Educational Data Mining Society, 2021
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually…
Descriptors: Mathematics Instruction, Teaching Methods, Intelligent Tutoring Systems, Error Patterns
Almut Ketzer-Nöltge, Editor; Nicola Würffel, Editor – Peter Lang Publishing Group, 2024
For over four decades, textbooks have been enhanced with digital components, and today, it is almost impossible to find a textbook that does not contain any. Does this mean that textbooks have been fully digitalized and that we have reached a point where the integration of digital media into textbooks is the norm? Since there is no clear consensus…
Descriptors: Textbooks, Electronic Books, Computer Uses in Education, Educational History
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Harmon, Jon; Warnakulasooriya, Rasil – International Educational Data Mining Society, 2019
The Additive Factor Model (AFM) is a cognitive diagnostic model that can be used to predict student performance on items in a context that allows for student learning. Within AFM, "skills" have a learning rate, and student acquisition of a skill depends only on the number of opportunities a student has had to exercise that skill and the…
Descriptors: Electronic Learning, Factor Analysis, Goodness of Fit, Item Response Theory
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Serhan, Derar; Almeqdadi, Farouq – International Journal of Technology in Education and Science, 2020
Enhancing students' conceptual understanding and increasing student motivation to effectively participate in classroom discussions are important for instructors of mathematics. Web-based homework management systems provide alternatives to the traditional pen-and-paper based approaches. In addition, these tools facilitate the creation of a…
Descriptors: Student Attitudes, Mathematics Instruction, Homework, Assignments
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VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2016
Although the Andes project produced many results over its 18 years of activity, this commentary focuses on its contributions to understanding how a goal-free user interface impacts the overall design and performance of a step-based tutoring system. Whereas a goal-aligned user interface displays relevant goals as blank boxes or empty locations that…
Descriptors: Computer Interfaces, Intelligent Tutoring Systems, Technology Uses in Education, Performance
Pearson, 2018
Pearson sought to explore whether the use of Mastering Physics, an online tutorial system used in higher education introductory physics courses, is related to students' results in exams and external standardized tests. This Research Report presents findings from two research studies we conducted with Penn State University, a school known for…
Descriptors: Physics, Science Instruction, Introductory Courses, Science Tests
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