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Yun Tang; Zhengfan Li; Guoyi Wang; Xiangen Hu – Interactive Learning Environments, 2023
To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with…
Descriptors: Electronic Learning, Learning Activities, Self Management, Intelligent Tutoring Systems
S. Sageengrana; S. Selvakumar; S. Srinivasan – Interactive Learning Environments, 2024
Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their…
Descriptors: Electronic Learning, Dropouts, Student Behavior, Student Interests
Seongyune Choi; Yeonju Jang; Hyeoncheol Kim – Interactive Learning Environments, 2024
Intelligent Personal Assistants (IPAs) are becoming more prevalent in daily and educational contexts, increasing the possibility of using them as learning partners that can provide more personalized and learner-centric learning opportunities. However, research has primarily focused on educational advantages that IPAs may provide, overlooking…
Descriptors: Intelligent Tutoring Systems, Foreign Countries, Technology Uses in Education, Independent Study
Di Zhang; Gwo-Jen Hwang; Shih-Ting Chu – Interactive Learning Environments, 2024
When encountering difficulties in conventional educational games, learners seldom self-regulate to discover and organize the learning content in the game environment. With the development of the human-computer interaction technology, computer agents are gradually being applied to educational games to provide personalized guidance or support to…
Descriptors: Intelligent Tutoring Systems, Educational Games, Technology Uses in Education, Academic Achievement
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Laureano-Cruces, Ana Lilia; Ramirez-Rodriguez, Javier; Mora-Torres, Martha; de Arriaga, Fernando; Escarela-Perez, Rafael – Interactive Learning Environments, 2010
In this paper behavior during the teaching-learning process is modeled by means of a fuzzy cognitive map. The elements used to model such behavior are part of a generic didactic model, which emphasizes the use of cognitive and operative strategies as part of the student-tutor interaction. Examples of possible initial scenarios for the…
Descriptors: Cognitive Mapping, Educational Technology, Teaching Methods, Cognitive Development
Kenny, Claire; Pahl, Claus – Interactive Learning Environments, 2009
Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We…
Descriptors: Feedback (Response), Active Learning, Educational Technology, Evaluation Methods

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