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Khajah, Mohammad; Lindsey, Robert V.; Mozer, Michael C. – International Educational Data Mining Society, 2016
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better.…
Descriptors: Bayesian Statistics, Data Analysis, Prediction, Intelligent Tutoring Systems
Gonzalez-Brenes, Jose P.; Mostow, Jack – International Educational Data Mining Society, 2012
This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them; and (ii) Student Modeling, which infers students' learning by observing student performance. The practical…
Descriptors: Intelligent Tutoring Systems, Academic Achievement, Bayesian Statistics, Tutors
Sudol, Leigh Ann; Rivers, Kelly; Harris, Thomas K. – International Educational Data Mining Society, 2012
In complex problem solving domains, correct solutions are often comprised of a combination of individual components. Students usually go through several attempts, each attempt reflecting an individual solution state that can be observed during practice. Classic metrics to measure student performance over time rely on counting the number of…
Descriptors: Problem Solving, Tutors, Feedback (Response), Probability
Wang, Yutao; Heffernan, Neil T. – International Educational Data Mining Society, 2012
The field of educational data mining has been using the Knowledge Tracing model, which only look at the correctness of student first response, for tracking student knowledge. Recently, lots of other features are studied to extend the Knowledge Tracing model to better model student knowledge. The goal of this paper is to analyze whether or not the…
Descriptors: Reaction Time, Students, Knowledge Level, Models
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Hafidi, Mohamed; Bensebaa, Tahar – International Journal of Information and Communication Technology Education, 2014
Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of the learner's multiple intelligences, the learner's skill level and the learner's feedback when implementing…
Descriptors: Intelligent Tutoring Systems, Models, Foreign Countries, Pretests Posttests
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Olsen, Jennifer K.; Belenky, Daniel M.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2014
While collaborative Intelligent Tutoring Systems (ITSs) have been designed for older students and have been shown to support sense-making behaviors, there has not been as much work on creating systems to support collaboration between elementary school students. We have developed and tested, with 84 students, individual and collaborative versions…
Descriptors: Intelligent Tutoring Systems, Elementary School Students, Fractions, Cooperative Learning
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Perry, S. Marshall – IGI Global, 2014
This chapter concerns a year-long, United States federally-funded evaluation of Educate Online, an online, at home, 1:1 tutoring program aimed at improving reading performance for middle school students who are below grade level. Participating students receive after-school instruction from teachers in real-time over Voice over Internet Protocol…
Descriptors: Program Evaluation, Intelligent Tutoring Systems, After School Programs, Reading Instruction
Pane, John F.; Griffin, Beth Ann; McCaffrey, Daniel F.; Karam, Rita; Daugherty, Lindsay; Phillips, Andrea – Grantee Submission, 2013
This large-scale effectiveness trial found a significant positive effect for high schools using Cognitive Tutor Algebra I (CTAI) in their second year of implementation, relative to similar schools that continued to use existing textbook-based algebra curricula. This positive result is important for educators and policymakers seeking ways to…
Descriptors: Mathematics Instruction, Algebra, Intelligent Tutoring Systems, Computer Software
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Li, Yatao; Zhao, Ke; Xu, Wei – International Journal of Information and Communication Technology Education, 2015
Intelligent tutoring systems (ITSs), which provide step-by-step guidance to students in problem-solving activities, have been shown to enhance student learning in a range of domains. However, they tend to be pre-established and cannot supply the tutoring function immediately from the diverse mathematical questions. The MITSAS (multiagent…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Foreign Countries, Algebra
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Bernacki, Matthew L.; Nokes-Malach, Timothy J.; Aleven, Vincent – Metacognition and Learning, 2015
Self-regulated learning (SRL) theorists propose that learners' motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational constructs as stable factors. In this study, self-efficacy was assessed frequently to observe its variability during learning and how learners'…
Descriptors: Self Efficacy, Metacognition, Cues, Algebra
Gobert, Janice D.; Kim, Yoon Jeon; Sao Pedro, Michael; Kennedy, Michael; Betts, Cameron – Grantee Submission, 2015
Many national policy documents underscore the importance of 21st century skills, including critical thinking. In parallel, recent American frameworks for K-12 Science education call for the development of critical thinking skills in science, also referred to as science inquiry skills/practices. Assessment of these skills is necessary, as indicated…
Descriptors: Learning Analytics, Science Education, Teaching Methods, 21st Century Skills
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Koedinger, Kenneth R.; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2016
Our 1997 article in "IJAIED" reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students' learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research…
Descriptors: Intelligent Tutoring Systems, Technology Uses in Education, Educational Technology, Algebra
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Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao – Interactive Learning Environments, 2016
To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…
Descriptors: Reading Strategies, Prediction, Models, Quasiexperimental Design
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Cargile, Lori A.; Harkness, Shelly Sheats – TechTrends: Linking Research and Practice to Improve Learning, 2014
Khan Academy (KA) is a free web-based intelligent tutor, which has been featured in countless media outlets for its potential to change mathematics instruction. The founder and executive director, Salman Khan, recommends that KA be used to personalize instruction, freeing up class time for engaging high yield activities like student discourse and…
Descriptors: Teaching Methods, Video Technology, Web Sites, Mathematics Instruction
Miwa, Kazuhisa; Kojima, Kazuaki; Terai, Hitoshi – International Association for Development of the Information Society, 2014
Tutoring systems provide students with various types of on-demand and context-sensitive hints. Students are required to consciously adapt their help-seeking behavior, proactively seek help in some situations, and solve problems independently without supports in other situations. We define the latter behavior as stoic behavior in hint seeking. In…
Descriptors: Help Seeking, Student Behavior, Cues, Goal Orientation
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