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Huang, Tao; Hu, Shengze; Yang, Huali; Geng, Jing; Liu, Sannyuya; Zhang, Hao; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…
Descriptors: Educational Technology, Prediction, Electronic Learning, Intelligent Tutoring Systems
Hyeon-Ah Kang; Adam Sales; Tiffany A. Whittaker – Grantee Submission, 2023
Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent…
Descriptors: Intelligent Tutoring Systems, Algebra, Educational Technology, Learning Processes
Rosmansyah, Yusep; Putro, Budi Laksono; Putri, Atina; Utomo, Nur Budi; Suhardi – Interactive Learning Environments, 2023
In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand…
Descriptors: Electronic Learning, Artificial Intelligence, Educational Technology, Technology Uses in Education
Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
Charles E. Jakobsche; Pitipat Kongsomjit; Conor Milson; Wenxing Wang; Chun-Kit Ngan – Journal of Chemical Education, 2023
The current work develops intelligent tutoring aspects for the DiscoverOChem learning platform. Intelligent tutoring systems are technology-based learning systems that can adapt the learning experience to better serve individual users. DiscoverOChem (www.discoverochem.com) is a free Internet-based platform for learning undergraduate-level organic…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Undergraduate Study
Badrinath, Anirudhan; Wang, Frederic; Pardos, Zachary – International Educational Data Mining Society, 2021
Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library…
Descriptors: Models, Markov Processes, Mathematics, Intelligent Tutoring Systems
Xiang Wu; Huanhuan Wang; Yongting Zhang; Baowen Zou; Huaqing Hong – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate…
Descriptors: Artificial Intelligence, Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
Weitekamp, Daniel, III.; Harpstead, Erik; MacLellan, Christopher J.; Rachatasumrit, Napol; Koedinger, Kenneth R. – International Educational Data Mining Society, 2019
Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners' performance in…
Descriptors: Computation, Models, Learning, Prediction
Grubišic, Ani; Žitko, Branko; Stankov, Slavomir – Journal of Technology and Science Education, 2020
In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is…
Descriptors: Electronic Learning, Educational Technology, Models, Intelligent Tutoring Systems
Goldberg, Benjamin; Amburn, Charles; Ragusa, Charlie; Chen, Dar-Wei – International Journal of Artificial Intelligence in Education, 2018
The U.S. Army is interested in extending the application of intelligent tutoring systems (ITS) beyond cognitive problem spaces and into psychomotor skill domains. In this paper, we present a methodology and validation procedure for creating expert model representations in the domain of rifle marksmanship. GIFT (Generalized Intelligent Framework…
Descriptors: Psychomotor Skills, Intelligent Tutoring Systems, Program Validation, Models
Albacete, Patricia; Jordan, Pamela; Lusetich, Dennis; Katz, Sandra; Chounta, Irene-Angelica; McLaren, Bruce M. – Grantee Submission, 2018
This paper discusses how a dialogue-based tutoring system makes decisions to proactively scaffold students during conceptual discussions about physics. The tutor uses a student model to predict the likelihood that the student will answer the next question in a dialogue script correctly. Based on these predictions, the tutor will, step by step,…
Descriptors: Intelligent Tutoring Systems, Scaffolding (Teaching Technique), Physics, Science Instruction
Tunjera, Nyarai; Chigona, Agnes – International Journal of Information and Communication Technology Education, 2020
The study examined how teacher educators are appropriating technological, pedagogical, and content knowledge (TPACK) and substitution, augmentation, modification, redefinition (SAMR) frameworks in their pre-service teacher preparation programmes. To ensure rigor, quality, and preparedness of pre-service teachers, there is a need to articulate…
Descriptors: Teacher Educators, Technological Literacy, Pedagogical Content Knowledge, Models
VanLehn, Kurt; Wetzel, Jon; Grover, Sachin; van de Sande, Brett – IEEE Transactions on Learning Technologies, 2017
Constructing models of dynamic systems is an important skill in both mathematics and science instruction. However, it has proved difficult to teach. Dragoon is an intelligent tutoring system intended to quickly and effectively teach this important skill. This paper describes Dragoon and an evaluation of it. The evaluation randomly assigned…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Skill Development
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes
Bull, Susan – Research and Practice in Technology Enhanced Learning, 2016
Today's technology-enabled learning environments are becoming quite different from those of a few years ago, with the increased processing power as well as a wider range of educational tools. This situation produces more data, which can be fed back into the learning process. Open learner models have already been investigated as tools to promote…
Descriptors: Educational Technology, Electronic Learning, Models, Computer Assisted Instruction