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50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing all 13 results
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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
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Weragama, Dinesha; Reye, Jim – International Journal of Artificial Intelligence in Education, 2014
Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Programming Languages
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Dela Rosa, Kevin; Eskenazi, Maxine – International Journal of Artificial Intelligence in Education, 2013
Self-assessment questionnaires have long been used in tutoring systems to help researchers measure and evaluate various aspects of a student's performance during learning activities. In this paper, we chronicle the efforts made in the REAP project, a language tutor developed to teach vocabulary to ESL students through reading activities, to…
Descriptors: Self Evaluation (Individuals), Questionnaires, Student Evaluation, Intelligent Tutoring Systems
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Gowda, Sujith M.; Baker, Ryan S.; Corbett, Albert T.; Rossi, Lisa M. – International Journal of Artificial Intelligence in Education, 2013
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning--learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions:…
Descriptors: Learning Processes, Transfer of Training, Outcomes of Education, Intelligent Tutoring Systems
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Lintean, Mihai; Rus, Vasile; Azevedo, Roger – International Journal of Artificial Intelligence in Education, 2012
This article describes the problem of detecting the student mental models, i.e. students' knowledge states, during the self-regulatory activity of prior knowledge activation in MetaTutor, an intelligent tutoring system that teaches students self-regulation skills while learning complex science topics. The article presents several approaches to…
Descriptors: Semantics, Intelligent Tutoring Systems, Prior Learning, Mathematics
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Chi, Min; VanLehn, Kurt; Litman, Diane; Jordan, Pamela – International Journal of Artificial Intelligence in Education, 2011
Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to…
Descriptors: Classroom Communication, Interaction, Reinforcement, Natural Language Processing
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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)
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Perez-Marin, Diana; Pascual-Nieto, Ismael – International Journal of Artificial Intelligence in Education, 2010
A student conceptual model can be defined as a set of interconnected concepts associated with an estimation value that indicates how well these concepts are used by the students. It can model just one student or a group of students, and can be represented as a concept map, conceptual diagram or one of several other knowledge representation…
Descriptors: Concept Mapping, Knowledge Representation, Models, Universities
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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 comprehension,…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables
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D'Mello, Sidney K.; Lehman, Blair; Person, Natalie – International Journal of Artificial Intelligence in Education, 2010
We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic…
Descriptors: Video Technology, Electronic Learning, Handheld Devices, Student Attitudes
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Weerasinghe, Amali; Mitrovic, Antonija; Martin, Brent – International Journal of Artificial Intelligence in Education, 2009
One of the critical factors contributing to the effectiveness of human tutoring is the conversational aspect of the instruction. Our goal is to develop a general model for supporting dialogues with menu-based input that could be used in both well- and ill-defined instructional tasks. We have previously studied how human tutors provide additional…
Descriptors: Intelligent Tutoring Systems, Dialogs (Language), Databases, Design
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Mitrovic, Antonija; Martin, Brent; Suraweera, Pramuditha; Zakharov, Konstantin; Milik, Nancy; Holland, Jay; McGuigan, Nicholas – International Journal of Artificial Intelligence in Education, 2009
Over the last decade, the Intelligent Computer Tutoring Group (ICTG) has implemented many successful constraint-based Intelligent Tutoring Systems (ITSs) in a variety of instructional domains. Our tutors have proven their effectiveness not only in controlled lab studies but also in real classrooms, and some of them have been commercialized.…
Descriptors: Foreign Countries, Investment, Intelligent Tutoring Systems, Artificial Intelligence
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Ogan, Amy; Aleven, Vincent; Jones, Christopher – International Journal of Artificial Intelligence in Education, 2009
Most successes in intelligent tutoring systems have come in well-defined domains like algebra or physics. We investigate how to support students in acquiring ill-defined skills of intercultural competence using an online environment that employs clips of feature films from a target culture. To test the effectiveness of a set of attention-focusing…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Perspective Taking, Cultural Awareness