Publication Date
| In 2015 | 1 |
| Since 2014 | 4 |
| Since 2011 (last 5 years) | 10 |
| Since 2006 (last 10 years) | 27 |
| Since 1996 (last 20 years) | 27 |
Descriptor
Source
| International Journal of… | 27 |
Author
| Aleven, Vincent | 4 |
| VanLehn, Kurt | 3 |
| Eskenazi, Maxine | 2 |
| Martin, Brent | 2 |
| McLaren, Bruce M. | 2 |
| Mitrovic, Antonija | 2 |
| Adamson, David | 1 |
| Anwar, Mohd | 1 |
| Ashley, Kevin | 1 |
| Azevedo, Roger | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 27 |
| Reports - Research | 21 |
| Reports - Descriptive | 5 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 27 |
| Postsecondary Education | 17 |
| High Schools | 2 |
| Elementary Secondary Education | 1 |
| Secondary Education | 1 |
Audience
Showing 1 to 15 of 27 results
Paquette, Luc; Lebeau, Jean-François; Beaulieu, Gabriel; Mayers, André – International Journal of Artificial Intelligence in Education, 2015
Model-tracing tutors (MTTs) have proven effective for the tutoring of well-defined tasks, but the pedagogical interventions they produce are limited and usually require the inclusion of pedagogical content, such as text message templates, in the model of the task. The capability to generate pedagogical content would be beneficial to MTT…
Descriptors: Intelligent Tutoring Systems, Intervention, Instruction, Automation
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
Adamson, David; Dyke, Gregory; Jang, Hyeju; Rosé, Carolyn Penstein – International Journal of Artificial Intelligence in Education, 2014
This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of…
Descriptors: Cooperative Learning, Scaffolding (Teaching Technique), Intelligent Tutoring Systems, Electronic Learning
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
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
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
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
Anwar, Mohd; Greer, Jim – International Journal of Artificial Intelligence in Education, 2012
An e-learning discussion forum, an essential component of today's e-learning systems, offers a platform for social learning activities. However, as learners participate in the discussion forum, privacy emerges as a major concern. Privacy concerns in social learning activities originate from one learner's inability to convey a desired presentation…
Descriptors: Foreign Countries, Electronic Learning, Socialization, Learning Activities
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
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
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
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)
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
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
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
Previous Page | Next Page »
Pages: 1 | 2
Peer reviewed
Direct link
