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Yoo, Jaebong; Kim, Jihie – International Journal of Artificial Intelligence in Education, 2014
Although many college courses adopt online tools such as Q&A online discussion boards, there is no easy way to measure or evaluate their effect on learning. As a part of supporting instructional assessment of online discussions, we investigate a predictive relation between characteristics of discussion contributions and student performance.…
Descriptors: Discussion Groups, Participation, Group Activities, Student Projects
Pareto, Lena – International Journal of Artificial Intelligence in Education, 2014
In this paper we will describe a learning environment designed to foster conceptual understanding and reasoning in mathematics among younger school children. The learning environment consists of 48 2-player game variants based on a graphical model of arithmetic where the mathematical content is intrinsically interwoven with the game idea. The…
Descriptors: Concept Formation, Mathematical Concepts, Mathematics Instruction, Educational Games
Baker, Ryan S. J. D.; Goldstein, Adam B.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Probability, Knowledge Level
Gong, Yue; Beck, Joseph E.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive…
Descriptors: Intelligent Tutoring Systems, Factor Analysis, Performance Factors, Models
Pardos, Zachary A.; Dailey, Matthew D.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
The well established, gold standard approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pre-test and post-test design. RCTs have been used in the intelligent tutoring community for decades to determine which questions and tutorial feedback work best. Practically speaking, however,…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Pretests Posttests, Educational Research
Khandaker, Nobel; Soh, Leen-Kiat – International Journal of Artificial Intelligence in Education, 2010
Two critical issues of the typical computer-supported collaborative learning (CSCL) systems are inappropriate selection of student groups and inaccurate assessment of individual contributions of the group members. Inappropriate selection of student groups often leads to ineffective and inefficient collaboration, while inaccurate assessment of…
Descriptors: Computer Uses in Education, Cooperative Learning, Selection, Grouping (Instructional Purposes)
McLaren, Bruce M.; Scheuer, Oliver; Miksatko, Jan – International Journal of Artificial Intelligence in Education, 2010
An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about topics presented by a teacher. These tools are aimed at teaching students how to discuss and argue, important skills not often taught in traditional classrooms. But how…
Descriptors: Artificial Intelligence, Cooperative Learning, Computer Mediated Communication, Discussion (Teaching Technique)
Muldner, Kasia; Conati, Cristina – International Journal of Artificial Intelligence in Education, 2010
Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Cognitive Psychology, Thinking Skills
Suraweera, Pramuditha; Mitrovic, Antonija; Martin, Brent – International Journal of Artificial Intelligence in Education, 2010
Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the system's intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better…
Descriptors: Intelligent Tutoring Systems, Programming, Engineering, Tutoring
Hayashi, Yusuke; Bourdeau, Jacqueline; Mizoguchi, Riichiro – International Journal of Artificial Intelligence in Education, 2009
This paper describes the achievements of an innovative eight-year research program first introduced in Mizoguchi and Bourdeau (2000), which was aimed at building a theory-aware authoring system by using ontological engineering. To date, we have proposed OMNIBUS, an ontology that comprehensively covers different learning/instructional theories and…
Descriptors: Foreign Countries, Theory Practice Relationship, Engineering, Teaching Methods

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