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Conrad Borchers; Hendrik Fleischer; David J. Yaron; Bruce M. McLaren; Katharina Scheiter; Vincent Aleven; Sascha Schanze – Journal of Science Education and Technology, 2025
Intelligent tutoring system (ITS) provides learners with step-by-step problem-solving support through scaffolding. Most ITSs have been developed in the USA and incorporate American instructional strategies. How do non-American students perceive and use ITS with different native problem-solving strategies? The present study compares Stoich Tutor,…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Learning Strategies, Protocol Analysis
Conrad Borchers; Jiayi Zhang; Hendrik Fleischer; Sascha Schanze; Vincent Aleven; Ryan S. Baker – Journal of Educational Data Mining, 2025
Think-aloud protocols are a standard method to study self-regulated learning (SRL) during learning by problem-solving. Advances in automated transcription and large language models (LLMs) have automated the transcription and labeling of SRL in these protocols, reducing manual effort. However, while effective in many emerging applications, previous…
Descriptors: Artificial Intelligence, Protocol Analysis, Learning Strategies, Classification
Conrad Borchers; Paulo F. Carvalho; Meng Xia; Pinyang Liu; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2023
In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Mathematics Education, Learning Processes
Conrad Borchers; Kexin Yang; Jionghao Lin; Nikol Rummel; Kenneth R. Koedinger; Vincent Aleven – International Educational Data Mining Society, 2024
Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act…
Descriptors: Artificial Intelligence, Peer Teaching, Tutoring, Technology Uses in Education
Tomohiro Nagashima; Elizabeth Ling; Bin Zheng; Anna N. Bartel; Elena M. Silla; Nicholas A. Vest; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2022
Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how "sustaining" or "interleaving" visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63…
Descriptors: Visual Aids, Scaffolding (Teaching Technique), Intelligent Tutoring Systems, Problem Solving
Tomohiro Nagashima; Stephanie Tseng; Elizabeth Ling; Anna N. Bartel; Nicholas A. Vest; Elena M. Silla; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2022
Learners' choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a "choice-based" intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an…
Descriptors: Middle School Students, Visual Aids, Intelligent Tutoring Systems, Independent Study
Vincent Aleven; Jori Blankestijn; LuEttaMae Lawrence; Tomohiro Nagashima; Niels Taatgen – Grantee Submission, 2022
Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…
Descriptors: Educational Technology, Artificial Intelligence, Problem Solving, Intelligent Tutoring Systems
Nicholas A. Vest; Elena M. Silla; Anna N. Bartel; Tomohiro Nagashima; Vincent Aleven; Martha W. Alibali – Grantee Submission, 2022
One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g., Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We…
Descriptors: Algebra, Mathematics Instruction, Intelligent Tutoring Systems, Middle School Students
Meng Xia; Robin Schmucker; Conrad Borchers; Vincent Aleven – Grantee Submission, 2025
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused…
Descriptors: Mastery Learning, Skill Development, Intelligent Tutoring Systems, Technology Uses in Education
Scaffolded Self-Explanation with Visual Representations Promotes Efficient Learning in Early Algebra
Tomohiro Nagashima; Anna N. Bartel; Stephanie Tseng; Nicholas A. Vest; Elena M. Silla; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2021
Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self- explanation, a novel form of instructional scaffolding in which visual representations are used to…
Descriptors: Visual Aids, Scaffolding (Teaching Technique), Mathematics Instruction, Algebra
Yanjin Long; Kenneth Holstein; Vincent Aleven – Grantee Submission, 2018
Accurately modeling individual students' knowledge growth is important in many applications of learning analytics. A key step is to decompose the knowledge targeted in the instruction into detailed knowledge components (KCs). We search for an accurate KC model for basic equation solving skills, using data from an intelligent tutoring system (ITS),…
Descriptors: Learning Processes, Mathematics Skills, Equations (Mathematics), Problem Solving

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