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Huaiya Liu; Yuyue Zhang; Jiyou Jia – IEEE Transactions on Learning Technologies, 2024
Intelligent tutoring systems (ITSs) aim to deliver personalized learning support to each learner, aligning with the educational aspiration of many countries, including China. ITSs' personalized support is mainly achieved by providing individual prompts to learners when they encounter difficulties in problem-solving. The guiding principles and…
Descriptors: Intelligent Tutoring Systems, Mathematics Achievement, Individualized Instruction, Foreign Countries
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
Xiaoli Huang; Wei Xu; Ruijia Liu – International Journal of Distance Education Technologies, 2025
This article presents a meta-analysis of the existing literature using Stata 18.0, focusing on the effects of ITSs on learning attitudes, knowledge acquisition, learner motivation, performance, problem-solving skills, test scores, and educational outcomes across different countries and educational levels (k = 30, g = 0.86). The findings suggest…
Descriptors: Intelligent Tutoring Systems, Outcomes of Education, Learning Motivation, Student Attitudes
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
Hannah Smith; Avery H. Closser; Erin Ottmar; Jenny Yun-Chen Chan – Applied Cognitive Psychology, 2022
Worked examples are effective learning tools for algebraic equation solving. However, they are typically presented in a static concise format, which only displays the major derivation steps in one static image. The current work explores how worked examples that vary in their extensiveness (i.e., detail) and degree of dynamic presentation (i.e.,…
Descriptors: Algebra, Mathematics Instruction, Equations (Mathematics), Problem Solving
Guojing Zhou – ProQuest LLC, 2020
In interactive e-learning environments such as Intelligent Tutoring Systems, there are pedagogical decisions to make at two main levels of granularity: whole problems and single steps. Here, we focus on making the problem-level decisions of worked example (WE) vs. problem solving (PS) and the step-level decisions of elicit vs. tell. More…
Descriptors: Educational Policy, Problem Solving, Learning Processes, Competence
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
Pai, Kai-Chih; Kuo, Bor-Chen; Liao, Chen-Huei; Liu, Yin-Mei – Educational Psychology, 2021
The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit 'multiplication and division of time expressions' was taught to 134 fifth-grade students in three types of instruction conditions: the intelligent tutoring system…
Descriptors: Dialogs (Language), Intelligent Tutoring Systems, Remedial Mathematics, Multiplication
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
Chen, Xingliang; Mitrovic, Antonija; Mathews, Moffat – IEEE Transactions on Learning Technologies, 2020
Problem solving, worked examples, and erroneous examples have proven to be effective learning activities in Intelligent Tutoring Systems (ITSs). However, it is generally unknown how to select learning activities adaptively in ITSs to maximize learning. In the previous work of A. Shareghi Najar and A. Mitrovic, alternating worked examples with…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Learning Activities, Educational Technology
Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics
Katz, Sandra; Albacete, Patricia; Chounta, Irene-Angelica; Jordan, Pamela; McLaren, Bruce M.; Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…
Descriptors: Physics, Science Instruction, Pretests Posttests, Scores
Rathod, Balraj B.; Murthy, Sahana; Bandyopadhyay, Subhajit – Journal of Chemical Education, 2019
"Is this solution pink enough?" is a persistent question when it comes to phenolphthalein-based titration experiments, one that budding, novice scientists often ask their instructors. Lab instructors usually answer the inquiry with remarks like, "Looks like you have overshot the end point", "Perhaps you should check the…
Descriptors: Handheld Devices, Telecommunications, Chemistry, Intelligent Tutoring Systems
Meng, Qingquan; Jia, Jiyou; Zhang, Zhiyong – Interactive Technology and Smart Education, 2020
Purpose: The purpose of this study is to verify the effect of smart pedagogy to facilitate the high order thinking skills of students and to provide the design suggestion of curriculum and intelligent tutoring systems in smart education. Design/methodology/approach: A smart pedagogy framework was designed. The quasi-experiment was conducted in a…
Descriptors: Thinking Skills, Instructional Effectiveness, Technology Integration, Intelligent Tutoring Systems
Matsuda, Noboru; Weng, Wenting; Wall, Natalie – International Journal of Artificial Intelligence in Education, 2020
The effect of metacognitive scaffolding for learning by teaching was investigated and compared against learning by being tutored. Three versions of an online learning environment for learning algebra equations were created: (1) APLUS that allows students to interactively teach a synthetic peer with a goal to have the synthetic peer pass the quiz…
Descriptors: Metacognition, Scaffolding (Teaching Technique), Tutoring, Intelligent Tutoring Systems

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