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Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi – International Journal of Distance Education Technologies, 2015
The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…
Descriptors: Electronic Learning, Context Effect, Handheld Devices, Computer Uses in Education
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Jacovina, Matthew E.; Snow, Erica L.; Allen, Laura K.; Roscoe, Rod D.; Weston, Jennifer L.; Dai, Jianmin; McNamara, Danielle S. – Grantee Submission, 2015
Intelligent tutoring systems (ITSs) have been successful at improving students' performance across a variety of domains. To help achieve this widespread success, researchers have identified important behavioral and performance measures that can be used to guide instruction and feedback. Most systems, however, do not present these measures to the…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Feedback (Response)
Hayashi, Yugo – International Educational Data Mining Society, 2015
The present study investigates web-based learning activities of undergraduate students who generate explanations about a key concept taught in a large-scale classroom. The present study used an online system with Pedagogical Conversational Agent (PCA), asked to explain about the key concept from different points and provided suggestions and…
Descriptors: Web Based Instruction, Learning Activities, Undergraduate Students, Intelligent Tutoring Systems
Luz, Bruno N.; Santos, Rafael; Alves, Bruno; Areão, Andreza S.; Yokoyama, Marcos H.; Guimarães, Marcelo P. – International Association for Development of the Information Society, 2015
The main purpose of this paper is to present the importance of Interactive Learning Objects (ILO) to improve the teaching-learning process by assuring a constant interaction among teachers and students, which in turn, allows students to be constantly supported by the teacher. The paper describes the ontology that defines the ILO available on the…
Descriptors: Resource Units, Metadata, Interaction, Learning Processes
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Lin, C.-C.; Guo, K.-H.; Lin, Y.-C. – Journal of Computer Assisted Learning, 2016
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
Descriptors: Remedial Instruction, Artificial Intelligence, Intelligent Tutoring Systems, Electronic Learning
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Dani, Anita; Nasser, Ramzi – Turkish Online Journal of Educational Technology - TOJET, 2016
The purpose of this paper is to determine potential identifiers of students' academic success in foundation mathematics course from the data logs of the intelligent tutor Assessment for Learning using Knowledge Spaces (ALEKS). A cross-sectional study design was used. A sample of 152 records, which accounts to approximately 60% of the population,…
Descriptors: Postsecondary Education, Mathematics Education, Intelligent Tutoring Systems, Technology Uses in Education
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Xiong, Xiaolu; Zhao, Siyuan; Van Inwegen, Eric G.; Beck, Joseph E. – International Educational Data Mining Society, 2016
Over the last couple of decades, there have been a large variety of approaches towards modeling student knowledge within intelligent tutoring systems. With the booming development of deep learning and large-scale artificial neural networks, there have been empirical successes in a number of machine learning and data mining applications, including…
Descriptors: Intelligent Tutoring Systems, Computer Software, Bayesian Statistics, Knowledge Level
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Lewin, Cathy; Smith, Andrew; Morris, Stephen; Craig, Elaine – Education Endowment Foundation, 2019
The Education Endowment Foundation (EEF) review of the impact of digital technology on learning, "The Impact of Digital Technology on Learning: A Summary for the Education Endowment Foundation. Full Report" (Higgins et al., 2012) (ED612174), found positive benefits but noted that how technology is used (the pedagogy) is key and that…
Descriptors: Technology Uses in Education, Educational Improvement, Influence of Technology, Foreign Countries
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Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao – IEEE Transactions on Learning Technologies, 2014
During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…
Descriptors: Cognitive Style, Pattern Recognition, Intelligent Tutoring Systems, Prediction
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Walker, Erin; Rummel, Nikol; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2014
Adaptive collaborative learning support (ACLS) involves collaborative learning environments that adapt their characteristics, and sometimes provide intelligent hints and feedback, to improve individual students' collaborative interactions. ACLS often involves a system that can automatically assess student dialogue, model effective and…
Descriptors: Algebra, Peer Teaching, Tutoring, Cooperative Learning
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San Pedro, Maria Ofelia Z.; de Baker, Ryan S. J.; Rodrigo, Ma. Mercedes T. – International Journal of Artificial Intelligence in Education, 2014
We investigate the relationship between students' affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student's answer is wrong despite knowing the skill required to provide the correct answer. We operationalize the…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, High School Students, Psychological Patterns
Tumenayu, Ogar Ofut; Shabalina, Olga; Kamaev, Valeriy; Davtyan, Alexander – International Association for Development of the Information Society, 2014
Recent research has shown that educational games positively motivate learning. However, there is a little evidence that they can trigger learning to a large extent if the game-play is supported by additional activities. We aim to support educational games development with an Agent-Based Technology (ABT) by using intelligent pedagogical agents that…
Descriptors: Educational Technology, Educational Games, Teaching Methods, Cooperative Learning
Harsley, Rachel – International Association for Development of the Information Society, 2014
This paper presents a novel classification scheme for Collaborative Intelligent Tutoring Systems (CITS), an emergent research field. The three emergent classifications of CITS are unstructured, semi-structured, and fully structured. While all three types of CITS offer opportunities to improve student learning gains, the full extent to which these…
Descriptors: Intelligent Tutoring Systems, Classification, Instructional Effectiveness, Educational Technology
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Erica L. Snow; Danielle S. McNamara; Matthew E. Jacovina; Laura K. Allen; Amy M. Johnson; Cecile A. Perret – Grantee Submission, 2014
Metacognitive awareness has been shown to be a critical skill for academic success. However, students often struggle to regulate this ability during learning tasks. The current study investigates how features designed to promote metacognitive awareness can be built into the game-based intelligent tutoring system (ITS) iSTART-2. College students…
Descriptors: Educational Technology, Intelligent Tutoring Systems, Game Based Learning, College Students
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Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
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