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Zhou, Guojing; Azizsoltani, Hamoon; Ausin, Markel Sanz; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2022
In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity. In this work, we focus on making decisions at "two levels": whole problems vs. single steps and explore three types of granularity: "problem-level only" ("Prob-Only"),…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Decision Making, Problem Solving
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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
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Zhang, Qiao; Maclellan, Christopher J. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…
Descriptors: Electronic Learning, Mastery Learning, Computer Simulation, Intelligent Tutoring Systems
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Hershcovits, Haviv; Vilenchik, Dan; Gal, Kobi – IEEE Transactions on Learning Technologies, 2020
This paper studies students engagement in e-learning environments in which students work independently and solve problems without external supervision. We propose a new method to infer engagement patterns of users in such self-directed environments. We view engagement as a continuous process in time, measured along chosen axes that are derived…
Descriptors: Electronic Learning, Problem Solving, Independent Study, Factor Analysis
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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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de Oliveira Costa Machado, Marcelo; Barrére, Eduardo; Souza, Jairo – International Journal of Distance Education Technologies, 2019
Adaptive curriculum sequencing (ACS) is still a challenge in the adaptive learning field. ACS is a NP-hard problem especially considering the several constraints of the student and the learning material when selecting a sequence from repositories where several sequences could be chosen. Therefore, this has stimulated several researchers to use…
Descriptors: Sequential Approach, Intelligent Tutoring Systems, Mathematics, Problem Solving
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Ifenthaler, Dirk, Ed.; Sampson, Demetrios G., Ed.; Isaías, Pedro, Ed. – Cognition and Exploratory Learning in the Digital Age, 2022
This book is about inclusivity and open education in the digital age. It reports the latest data on this topic from the 2021 Cognition and Exploratory Learning in the Digital Age (CELDA) conference. This annual conference focuses on challenges pertaining to the evolution of the learning process, the role of pedagogical approaches and the progress…
Descriptors: Teaching Methods, Educational Innovation, Educational Technology, Technology Uses in Education
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Kamsa, Imane; Elouahbi, Rachid; El Khoukhi, Fatima – Journal of Information Technology Education: Research, 2017
Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners' failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these…
Descriptors: Foreign Countries, Graduate Students, Masters Programs, Learning Disabilities
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Hafidi, Mohamed; Bensebaa, Taher – International Journal of Distance Education Technologies, 2015
The majority of adaptive and intelligent tutoring systems (AITS) are dedicated to a specific domain, allowing them to offer accurate models of the domain and the learner. The analysis produced from traces left by the users is didactically very precise and specific to the domain in question. It allows one to guide the learner in case of difficulty…
Descriptors: Intelligent Tutoring Systems, Foreign Countries, Interdisciplinary Approach, Universities
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Maloy, Robert W.; Razzaq, Leena; Edwards, Sharon A. – Journal of Interactive Learning Research, 2014
This study explored the use of an online mathematics tutoring system in eight fourth grade classrooms in two Massachusetts communities--a small rural city with a low 2010 Adequate Yearly Progress (AYP) math performance rating and a small suburban district with a high 2010 AYP math performance rating. 165 fourth graders completed 11 modules…
Descriptors: Intelligent Tutoring Systems, Multimedia Instruction, Mathematics Instruction, Elementary School Mathematics
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Kim, Min Kyu – Educational Technology Research and Development, 2012
It is generally accepted that the cognitive development for a wide range of students can be improved through adaptive instruction-learning environments optimized to suit individual needs (e.g., Cronbach, Am Psychol 12:671-684, 1957; Lee and Park, in Handbook of research for educational communications and technology, Taylor & Francis Group,…
Descriptors: Expertise, Problem Solving, Cognitive Development, Student Evaluation
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Lehman, Blair; D'Mello, Sidney; Graesser, Art – Internet and Higher Education, 2012
Folk wisdom holds that being confused is detrimental to learning. However, research on emotions and learning suggest a somewhat more complex relationship between confusion and learning outcomes. In fact, it has been proposed that impasses that trigger states of cognitive disequilibrium and confusion can create opportunities for deep learning of…
Descriptors: Feedback (Response), Learning Strategies, Problem Solving, Intervention
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Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels – Computers & Education, 2013
Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem. But does greater freedom mean that students…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Algebra, Mathematics Instruction
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Kiesmuller, Ulrich – ACM Transactions on Computing Education, 2009
At schools special learning and programming environments are often used in the field of algorithms. Particularly with regard to computer science lessons in secondary education, they are supposed to help novices to learn the basics of programming. In several parts of Germany (e.g., Bavaria) these fundamentals are taught as early as in the seventh…
Descriptors: Foreign Countries, Feedback (Response), Secondary School Students, Research Methodology
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D'Mello, Sidney K.; Lehman, Blair; Person, Natalie – International Journal of Artificial Intelligence in Education, 2010
We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic…
Descriptors: Video Technology, Electronic Learning, Handheld Devices, Student Attitudes
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