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Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
Xu, Zhihong; Wijekumar, Kausalai; Lin, Shuqiong; Yang, Xinyuan; Nan, Bo – Reading Psychology, 2022
The difficulties with developing high-order reading comprehension skills negatively impact academic success across fields. The current study investigated whether the text structure strategy instruction, delivered through the Intelligent Tutoring of Structure Strategy (ITSS) to adult English learners as a foreign language (EFL), can improve reading…
Descriptors: Web Based Instruction, English (Second Language), Second Language Learning, Second Language Instruction
Vandewaetere, Mieke; Vandercruysse, Sylke; Clarebout, Geraldine – Educational Technology Research and Development, 2012
Research on computer-based adaptive learning environments has shown exemplary growth. Although the mechanisms of effective adaptive instruction are unraveled systematically, little is known about the relative effect of learners' perceptions of adaptivity in adaptive learning environments. As previous research has demonstrated that the learners'…
Descriptors: Intelligent Tutoring Systems, Predictor Variables, Student Attitudes, Student Motivation
Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction
Hausmann, Robert G. M.; VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2010
Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional "content" that does not exist in the instructional materials. Second, when compared to…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables

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