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Kole Norberg; Husni Almoubayyed; Stephen Fancsali – International Educational Data Mining Society, 2025
Solving a math word problem (MWP) requires understanding the mathematical components of the problem and an ability to decode the text. For some students, lower reading comprehension skills may make engagement with the mathematical content more difficult. Readability formulas (e.g., Flesch Reading Ease) are frequently used to assess reading…
Descriptors: Mathematics Instruction, Word Problems (Mathematics), Problem Solving, Reading Skills
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Natalie Brezack; Melissa Lee; Kelly Collins; Wynnie Chan; Mingyu Feng – Grantee Submission, 2025
Students' effort and emotions are important contributors to math learning. In a recent study evaluating the efficacy of MathSpring, a scalable web-based intelligent tutoring system that provides students with personalized math problems and affective support, system usage data were collected for 804 U.S. 10-12-year-olds. To understand the patterns…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Behavior Patterns, Student Behavior
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Shakya, Anup; Rus, Vasile; Venugopal, Deepak – International Educational Data Mining Society, 2023
Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can…
Descriptors: Equal Education, Mathematics Education, Word Problems (Mathematics), Problem Solving
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
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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
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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
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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
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Çetin, Ismail; Erumit, Ali Kürsat; Nabiyev, Vasif; Karal, Hasan; Kosa, Temel; Kokoc, Mehmet – Participatory Educational Research, 2023
This study aims to examine the contribution of ArtiBos, which is designed as a Gamified Adaptive Intelligent Tutoring System for students' problem-solving skills. In the study, first of all, the system's design features to improve problem-solving skills were examined, and then the effect of the system on problem-solving skills was evaluated. The…
Descriptors: Gamification, Intelligent Tutoring Systems, Problem Solving, High School Students
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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
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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
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VanLehn, Kurt; Burkhardt, Hugh; Cheema, Salman; Kang, Seokmin; Pead, Daniel; Schoenfeld, Alan; Wetzel, Jon – Interactive Learning Environments, 2021
Mathematics is often taught by explaining an idea, then giving students practice in applying it. Tutoring systems can increase the effectiveness of this method by monitoring the students' practice and giving feedback. However, math can also be taught by having students work collaboratively on problems that lead them to discover the idea. Here,…
Descriptors: Intelligent Tutoring Systems, Cooperative Learning, Mathematics Instruction, Instructional Effectiveness
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
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Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
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de Morais, Felipe; Jaques, Patricia A. – Informatics in Education, 2022
Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers' keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load,…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Educational Technology, Technology Uses in Education
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Chiu, Mei-Shiu – Journal of Educational Data Mining, 2020
This study aims to identify effective affective states and behaviors of middle-school students' online mathematics learning in predicting their choices to study science, technology, engineering, and mathematics (STEM) in higher education based on a "positive-affect-to-success hypothesis." The dataset (591 students and 316,974 actions)…
Descriptors: Gender Differences, Predictor Variables, STEM Education, Course Selection (Students)
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