NotesFAQContact Us
Collection
Advanced
Search Tips
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

Audience
Showing 31 to 45 of 67 results
Peer reviewed Peer reviewed
Direct linkDirect link
Miller, L. D.; Soh, Leen-Kiat; Samal, Ashok; Nugent, Gwen – International Journal of Artificial Intelligence in Education, 2012
Learning objects (LOs) are digital or non-digital entities used for learning, education or training commonly stored in repositories searchable by their associated metadata. Unfortunately, based on the current standards, such metadata is often missing or incorrectly entered making search difficult or impossible. In this paper, we investigate…
Descriptors: Computer Science Education, Metadata, Internet, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Boyer, Kristy Elizabeth; Phillips, Robert; Ingram, Amy; Ha, Eun Young; Wallis, Michael; Vouk, Mladen; Lester, James – International Journal of Artificial Intelligence in Education, 2011
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning…
Descriptors: Markov Processes, Intelligent Tutoring Systems, Tutoring, Program Effectiveness
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Rowe, Jonathan P.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C. – International Journal of Artificial Intelligence in Education, 2011
A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to…
Descriptors: Problem Solving, Educational Technology, Virtual Classrooms, Educational Environment
Peer reviewed Peer reviewed
Direct linkDirect link
Stamper, John; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2011
The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation…
Descriptors: Cues, Prompting, Learning Strategies, Teaching Methods
Peer reviewed Peer reviewed
Direct linkDirect link
Arroyo, Ivon; Royer, James M.; Woolf, Beverly P. – International Journal of Artificial Intelligence in Education, 2011
This article integrates research in intelligent tutors with psychology studies of memory and math fluency (the speed to retrieve or calculate answers to basic math operations). It describes the impact of computer software designed to improve either strategic behavior or math fluency. Both competencies are key to improved performance and both…
Descriptors: Computer Software, Short Term Memory, Tutors, Mathematics Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
du Boulay, Benedict; Avramides, Katerina; Luckin, Rosemary; Martinez-Miron, Erika; Rebolledo-Mendez, Genaro; Carr, Amanda – International Journal of Artificial Intelligence in Education, 2010
This paper describes a Conceptual Framework underpinning "Systems that Care" in terms of educational systems that take account of motivation, metacognition and affect, in addition to cognition. The main focus is on "motivation," as learning requires the student to put in effort and be engaged, in other words to be motivated to learn. But…
Descriptors: Learning Motivation, Metacognition, Affective Behavior, Schemata (Cognition)
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
Chieu, Vu Minh; Luengo, Vanda; Vadcard, Lucile; Tonetti, Jerome – International Journal of Artificial Intelligence in Education, 2010
Cognitive approaches have been used for student modeling in intelligent tutoring systems (ITSs). Many of those systems have tackled fundamental subjects such as mathematics, physics, and computer programming. The change of the student's cognitive behavior over time, however, has not been considered and modeled systematically. Furthermore, the…
Descriptors: Foreign Countries, Medical Students, Surgery, Human Body
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Heilman, Michael; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine; Juffs, Alan; Wilson, Lois – International Journal of Artificial Intelligence in Education, 2010
The REAP tutoring system provides individualized and adaptive English as a Second Language vocabulary practice. REAP can automatically personalize instruction by providing practice readings about topics that match interests as well as domain-based, cognitive objectives. While most previous research on motivation in intelligent tutoring…
Descriptors: Incentives, Cognitive Objectives, Intelligent Tutoring Systems, Motivation
Pages: 1  |  2  |  3  |  4  |  5