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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
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
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Mingyu Feng; Natalie Brezack; Chunwei Huang; Melissa Lee; Megan Schneider; Kelly Collins; Wynnie Chan – Society for Research on Educational Effectiveness, 2024
Background/Context: Math education remains a critical focus for national education improvement. As a solution, districts in the U.S. are investing in math education technologies. Research has demonstrated the potential of these technologies to close achievement gaps (e.g., Pape et al., 2012; Roschelle et al., 2016). Student math achievement is…
Descriptors: Mathematics Education, Problem Solving, Educational Technology, Technology Uses in Education
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Viktor Wang, Editor – IGI Global, 2025
Artificial Intelligence (AI) integration in andragogical education offers significant enhancements to the learning experience for adult learners. By utilizing AI-powered platforms, instructors can provide personalized learning paths that adapt to the unique needs, interests, and goals of each individual. These systems can analyze performance data…
Descriptors: Andragogy, Artificial Intelligence, Computer Software, Technology Integration
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Mamcenko, Jelena; Kurilovas, Eugenijus; Krikun, Irina – Informatics in Education, 2019
The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for…
Descriptors: Case Method (Teaching Technique), Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
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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
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Fletcher, J. D. – Technology, Instruction, Cognition and Learning, 2018
Computer technology has been used for over 50 years to tailor learning experiences to the needs and interests of individual learners at all levels of instruction. It provides adaptation and individualization that is difficult, if not impossible to apply in a classroom of 20-30 students. This article provides a brief background and discussion about…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Public Agencies, Information Technology
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Aravind, Vasudeva Rao; Croyle, Kevin – Malaysian Online Journal of Educational Technology, 2017
Students learn scientific concepts and mathematical calculations relating to scientific principles by repetition and reinforcement. Teachers and instructors cannot practically spend the long time required during tutorials to patiently teach students the calculations. Usually, teachers assign homework to provide practice to students, hoping that…
Descriptors: Physics, College Science, Computation, Scientific Concepts
Ford, Christopher Jay – ProQuest LLC, 2018
Personalized learning for students has been an emerging trend which seeks to support teaching and learning in the 21st century (Netoch, 2017). This study identified beliefs of secondary educators on the different aspects of personalized learning for students. Areas covered in this study included teacher professional development on the personalized…
Descriptors: Secondary School Teachers, Teacher Attitudes, Beliefs, Individualized Instruction
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Shen, Shitian; Chi, Min – International Educational Data Mining Society, 2017
One of the most challenging tasks in the field of Educational Data Mining (EDM) is to cluster students directly based on system-student sequential moment-to-moment interactive trajectories. The objective of this study is to build a general temporal clustering framework that captures the distinct characteristics of students' sequential behaviors…
Descriptors: Sequential Approach, Cluster Grouping, Interaction, Student Behavior
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Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel – International Educational Data Mining Society, 2016
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally…
Descriptors: Intelligent Tutoring Systems, Markov Processes, Models, Teaching Methods
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Walkington, Candace; Bernacki, Matthew L. – Journal of Experimental Education, 2018
Instruction can be made relevant to students when it draws upon and utilizes their interests, experiences, and "funds of knowledge" in productive ways to support classroom learning. This approach has been referred to as "context personalization." In this paper, we discuss the cognitive basis of personalization interventions,…
Descriptors: Individualized Instruction, Instructional Design, Relevance (Education), Cognitive Processes
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Bernacki, Matthew L.; Walkington, Candace – Journal of Educational Psychology, 2018
Context personalization--the incorporation of students' out-of-school interests into learning tasks--has recently been shown to positively affect students' situational interest and their performance and learning in mathematics. However, few studies have shown effects on both interest and achievement, drawing into question whether context…
Descriptors: High School Students, Student Interests, Individualized Instruction, Mathematics Instruction
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Hooshyar, Danial; Ahmad, Rodina Binti; Yousefi, Moslem; Fathi, Moein; Abdollahi, Abbas; Horng, Shi-Jinn; Lim, Heuiseok – Educational Technology Research and Development, 2016
Nowadays, intelligent tutoring systems are considered an effective research tool for learning systems and problem-solving skill improvement. Nonetheless, such individualized systems may cause students to lose learning motivation when interaction and timely guidance are lacking. In order to address this problem, a solution-based intelligent…
Descriptors: Intelligent Tutoring Systems, Technology Integration, Educational Games, Formative Evaluation
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Booth, Julie L.; Koedinger, Kenneth R.; Newton, Kristie J.; Lange, Karin E. – Society for Research on Educational Effectiveness, 2013
A plethora of laboratory studies have shown that including the study of worked examples during problem-solving practice improves learning (Sweller, 1999; Sweller & Cooper, 1985). While most worked-example research focuses on the use of correct examples, recent work suggests that asking children to explain a combination of correct and incorrect…
Descriptors: Individualized Instruction, Middle School Students, Grade 8, Algebra
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