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Polsley, Seth; Powell, Larry; Kim, Hong-Hoe; Thomas, Xien; Liew, Jeffrey; Hammond, Tracy – International Journal of Artificial Intelligence in Education, 2022
Children's fine motor skills are linked not only to drawing ability but also to cognitive, social-emotional, self-regulatory, and academic development Suggate et al. "Journal of Research in Reading," 41(1), 1-19 (2018), Benedetti et al. (2014), Liew et al. "Early Education & Development," 22(4), 549-573 (2011), Liew (2012)…
Descriptors: Psychomotor Skills, Child Development, Computer Uses in Education, Handheld Devices
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Chau, Hung; Labutov, Igor; Thaker, Khushboo; He, Daqing; Brusilovsky, Peter – International Journal of Artificial Intelligence in Education, 2021
The increasing popularity of digital textbooks as a new learning media has resulted in a growing interest in developing a new generation of "adaptive textbooks" that can help readers to learn better through adapting to the readers' learning goals and the current state of knowledge. These adaptive textbooks are most frequently powered by…
Descriptors: Automation, Textbooks, Computer Uses in Education, Artificial Intelligence
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Liaqat, Amna; Munteanu, Cosmin; Demmans Epp, Carrie – International Journal of Artificial Intelligence in Education, 2021
300,000 immigrants move to Canada each year in search of better economic opportunities, and many have limited English language skills. Improving written literacy of newcomers can enhance education, employment, or social integration opportunities. However, frequent, timely, and personalized feedback is not always possible for immigrants. Online…
Descriptors: Adult Students, English Language Learners, Feedback (Response), Automation
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Lawson, Alyssa P.; Mayer, Richard E.; Adamo-Villani, Nicoletta; Benes, Bedrich; Lei, Xingyu; Cheng, Justin – International Journal of Artificial Intelligence in Education, 2021
There has been much research on the effectiveness of animated pedagogical agents in an educational context, however there is little research about how the emotions they display contribute to a learner's understanding of the lesson. The positivity principle suggests that learners should learn better from instructors with positive emotions compared…
Descriptors: Psychological Patterns, Animation, Recognition (Psychology), Artificial Intelligence
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Kumar, Vivekanandan S.; Boulanger, David – International Journal of Artificial Intelligence in Education, 2021
This article investigates the feasibility of using automated scoring methods to evaluate the quality of student-written essays. In 2012, Kaggle hosted an Automated Student Assessment Prize contest to find effective solutions to automated testing and grading. This article: a) analyzes the datasets from the contest -- which contained hand-graded…
Descriptors: Automation, Scoring, Essays, Writing Evaluation
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Lajoie, Susanne P. – International Journal of Artificial Intelligence in Education, 2021
I first met Jim Greer at the NATO Advanced Study Institute on Syntheses of Instructional Sciences and Computing Science for Effective Instructional Computing Systems in 1990 in Calgary, Canada. It was during this meeting that I came to realize that Jim was one of those rare individuals that could help "translate" computer science…
Descriptors: Models, Student Characteristics, Artificial Intelligence, Computer Uses in Education
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du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2021
Mark and Greer's ("International Journal of Artificial Intelligence in Education," 4(2/3), 129-153, 1993) review was very influential in setting out effective goals and methods for evaluating adaptive educational systems of all kinds. A later review brought the story up to date (Greer, "International Journal of Artificial…
Descriptors: Artificial Intelligence, Computer Uses in Education, Evaluation Methods, Student Satisfaction
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Nguyen, Huy; Xiong, Wenting; Litman, Diane – International Journal of Artificial Intelligence in Education, 2017
A peer-review system that automatically evaluates and provides formative feedback on free-text feedback comments of students was iteratively designed and evaluated in college and high-school classrooms. Classroom assignments required students to write paper drafts and submit them to a peer-review system. When student peers later submitted feedback…
Descriptors: Computer Uses in Education, Computer Mediated Communication, Feedback (Response), Peer Evaluation
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Tansomboon, Charissa; Gerard, Libby F.; Vitale, Jonathan M.; Linn, Marcia C. – International Journal of Artificial Intelligence in Education, 2017
Supporting students to revise their written explanations in science can help students to integrate disparate ideas and develop a coherent, generative account of complex scientific topics. Using natural language processing to analyze student written work, we compare forms of automated guidance designed to motivate productive revision and help…
Descriptors: Automation, Guidance, Revision (Written Composition), Natural Language Processing
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
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Pelánek, Radek; Jarušek, Petr – International Journal of Artificial Intelligence in Education, 2015
Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students' answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Interaction, Problem Solving
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Blanchard, Emmanuel G. – International Journal of Artificial Intelligence in Education, 2015
This paper investigates international representations in the Artificial Intelligence in Education (AIED) research field. Its methodological and theoretical groundings are inspired by Arnett (2008) and Henrich et al. (2010a) who addressed the same issue in psychology, and respectively a) discovered massive imbalances in representation in top-tier…
Descriptors: Artificial Intelligence, Educational Research, Conferences (Gatherings), Culture
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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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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
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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
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