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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
Anirudhan Badrinath; Zachary Pardos – Journal of Educational Data Mining, 2025
Bayesian Knowledge Tracing (BKT) is a well-established model for formative assessment, with optimization typically using expectation maximization, conjugate gradient descent, or brute force search. However, one of the flaws of existing optimization techniques for BKT models is convergence to undesirable local minima that negatively impact…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Problem Solving, Audience Response Systems
Juan Zheng; Shan Li; Tingting Wang; Susanne P. Lajoie – International Journal of Educational Technology in Higher Education, 2024
Emotions play a crucial role in the learning process, yet there is a scarcity of studies examining emotion dynamics in problem-solving with fine-grained data and advanced tools. This study addresses this gap by investigating the emotional trajectories during self-regulated learning (SRL) phases (i.e., forethought, performance, and self-reflection)…
Descriptors: Medical Students, Problem Solving, Intelligent Tutoring Systems, Nonverbal Communication
Qian Xu – Discover Education, 2024
This research suggests a methodology to examine the effectiveness Artificial Intelligence (AI) on the cognitive abilities of college students so that future researchers can utilize this experimental project to focus on how AI-powered Intelligent Tutoring Systems (ITSs) affect learning outcomes. As AI continues to revolutionize all walks of life,…
Descriptors: Artificial Intelligence, Cognitive Ability, College Students, Intelligent Tutoring Systems

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
Guozhu Ding; Xiangyi Shi; Shan Li – Education and Information Technologies, 2024
In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…
Descriptors: Programming, Computer Science Education, Classification, Graphs
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
Jiyou Jia; Tianrui Wang; Yuyue Zhang; Guangdi Wang – Asia Pacific Journal of Education, 2024
In designing an intelligent tutoring system, a core area of the application of AI in education, tips from the system or virtual tutors are crucial in helping students solve difficult questions in disciplines like mathematics. Traditionally, the manual design of general tips by teachers is time-consuming and error-prone. Generative AI, like…
Descriptors: Problem Solving, Artificial Intelligence, Learning Processes, Prompting
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
Xuanyan Zhong; Zehui Zhan – Interactive Technology and Smart Education, 2025
Purpose: The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners' computational thinking. Design/methodology/approach: By…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming, Independent Study
Melissa Lee; Chun-Wei Huang; Kelly Collins; Mingyu Feng – Grantee Submission, 2025
Math anxiety has been found to negatively correlate with math achievement, affecting students' choices to take fewer math classes and avoid math educational opportunities. Educational technology tools can ameliorate some of the negative effects of math anxiety. We examined students' math anxiety, effort in an educational technology platform, and…
Descriptors: Correlation, Mathematics Anxiety, Mathematics Achievement, Outcomes of Education
Ismail Celik; Egle Gedrimiene; Signe Siklander; Hanni Muukkonen – Australasian Journal of Educational Technology, 2024
Twenty-first-century skills should be integrated into higher education to prepare students for complex working-life challenges. Artificial intelligence (AI)-powered tools have the potential to optimise skill development among higher education students. Therefore, it is important to conceptualise relevant affordances of AI systems for 21st-century…
Descriptors: Artificial Intelligence, 21st Century Skills, Higher Education, Educational Research
Yung-Hsiang Hu – Education and Information Technologies, 2024
In this study, a generative artificial intelligence (AI)-assisted Think-Aloud Pair Problem-Solving (TAPPS) learning strategy was introduced to support ethical dilemma-related problem-solving learning activities. Then, an interactive virtual learning companion system was developed and tested in a business ethics course to evaluate the efficacy of…
Descriptors: Ethics, Problem Solving, Thinking Skills, Verbal Communication
Suwicha Wittayakom; Chintana Kanjanavisutt; Methinee Wongwanich Rumpagaporn – Higher Education Studies, 2024
This systematic literature review explores the implementation and effectiveness of active learning approaches in online training environments. The rapid growth of online education necessitates strategies that enhance learner engagement and improve educational outcomes. The review identifies various active learning techniques, such as discussions,…
Descriptors: Online Courses, Training, Active Learning, Learning Strategies
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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