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Damacharla, Praveen; Dhakal, Parashar; Stumbo, Sebastian; Javaid, Ahmad Y.; Ganapathy, Subhashini; Malek, David A.; Hodge, Douglas C.; Devabhaktuni, Vijay – International Journal of Artificial Intelligence in Education, 2019
As part of a perennial project, our team is actively engaged in developing new synthetic assistant (SA) technologies to assist in training combat medics and medical first responders. It is critical that medical first responders are well trained to deal with emergencies more effectively. This would require real-time monitoring and feedback for each…
Descriptors: Emergency Medical Technicians, Intelligent Tutoring Systems, Instructional Effectiveness, Performance
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Ayedoun, Emmanuel; Hayashi, Yuki; Seta, Kazuhisa – International Journal of Artificial Intelligence in Education, 2019
This paper describes an embodied conversational agent enhanced with specific conversational strategies aiming to foster learners' readiness towards communication in a second language (L2). Willingness to communicate (WTC) in a second language is believed to have a direct and sustained influence on learners' actual usage frequency of the target…
Descriptors: Second Language Learning, Anxiety, Communication (Thought Transfer), Intelligent Tutoring Systems
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Walkington, Candace; Bernacki, Matthew L. – International Journal of Artificial Intelligence in Education, 2019
Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students' individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of…
Descriptors: Algebra, Student Interests, Mathematics Instruction, Intelligent Tutoring Systems
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Tärning, Betty; Silvervarg, Annika; Gulz, Agneta; Haake, Magnus – International Journal of Artificial Intelligence in Education, 2019
This study examines the effects of teachable agents' expressed self-efficacy on students. A total of 166 students, 10- to 11-years-old, used a teachable agent-based math game focusing on the base-ten number system. By means of data logging and questionnaires, the study compared the effects of high vs. low agent self-efficacy on the students'…
Descriptors: Self Efficacy, Elementary School Students, Intelligent Tutoring Systems, Mathematics Instruction
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Taub, Michelle; Azevedo, Roger – International Journal of Artificial Intelligence in Education, 2019
The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students' (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the…
Descriptors: Prior Learning, Eye Movements, Metacognition, Learning Processes
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Chase, Catherine C.; Connolly, Helena; Lamnina, Marianna; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2019
A successful instructional method is to engage learners with exploratory problem-solving before providing explanations of the canonical solutions and foundational concepts. A key question is whether and what type of guidance will lead learners to explore more productively and how this guidance will affect subsequent learning and transfer. We…
Descriptors: Computer Assisted Instruction, Teaching Methods, Learner Engagement, Problem Solving
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Ullmann, Thomas Daniel – International Journal of Artificial Intelligence in Education, 2019
Reflective writing is an important educational practice to train reflective thinking. Currently, researchers must manually analyze these writings, limiting practice and research because the analysis is time and resource consuming. This study evaluates whether machine learning can be used to automate this manual analysis. The study investigates…
Descriptors: Reflection, Writing (Composition), Writing Evaluation, Automation
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Doble, Christopher; Matayoshi, Jeffrey; Cosyn, Eric; Uzun, Hasan; Karami, Arash – International Journal of Artificial Intelligence in Education, 2019
A large-scale simulation study of the assessment effectiveness of a particular instantiation of knowledge space theory is described. In this study, data from more than 700,000 actual assessments in mathematics using the ALEKS (Assessment and LEarning in Knowledge Spaces) software were used to determine response probabilities for the same number of…
Descriptors: Test Reliability, Adaptive Testing, Mathematics Tests, Computer Assisted Testing
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Leo, J.; Kurdi, G.; Matentzoglu, N.; Parsia, B.; Sattler, U.; Forge, S.; Donato, G.; Dowling, W. – International Journal of Artificial Intelligence in Education, 2019
Designing good multiple choice questions (MCQs) for education and assessment is time consuming and error-prone. An abundance of structured and semi-structured data has led to the development of automatic MCQ generation methods. Recently, ontologies have emerged as powerful tools to enable the automatic generation of MCQs. However, current question…
Descriptors: Multiple Choice Tests, Test Items, Automation, Test Construction
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Wu, Wen; Chen, Li; Yang, Qingchang; Li, You – International Journal of Artificial Intelligence in Education, 2019
Communication tools have been popular in web-based learning systems because of their ability to promote the interaction and potentially alleviate the high dropout issue. In recent years, with the increased awareness among researchers about the individual difference of the students, more and more personalized learning supports have been developed.…
Descriptors: Personality, Inferences, Student Behavior, Computer Mediated Communication
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Johnson, W. Lewis – International Journal of Artificial Intelligence in Education, 2019
Cloud computing offers developers of learning environments access to unprecedented amounts of learner data. This makes possible "data-driven development" (D[superscript 3]) of learning environments. In the D[superscript 3] approach the learning environment is a data collection tool as well a learning tool. It continually collects data…
Descriptors: Foreign Countries, Data Use, English (Second Language), Second Language Instruction
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Yeung, Chun-Kit; Yeung, Dit-Yan – International Journal of Artificial Intelligence in Education, 2019
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of…
Descriptors: Data Analysis, Careers, Prediction, Employment
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Pandarova, Irina; Schmidt, Torben; Hartig, Johannes; Boubekki, Ahcène; Jones, Roger Dale; Brefeld, Ulf – International Journal of Artificial Intelligence in Education, 2019
Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner's evolving language…
Descriptors: Intelligent Tutoring Systems, Difficulty Level, Cues, Second Language Learning
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Chen, Xingliang; Mitrovic, Antonija; Mathews, Moffat – International Journal of Artificial Intelligence in Education, 2019
Agency refers to the level of control the student has over learning. Most studies on agency in computer-based learning environments have been conducted in the context of educational games and multimedia learning, while there is little research done in the context of learning with Intelligent Tutoring Systems (ITSs). We conducted a study in the…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Educational Games, Independent Study
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Price, Thomas W.; Dong, Yihuan; Zhi, Rui; Paaßen, Benjamin; Lytle, Nicholas; Cateté, Veronica; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2019
In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different…
Descriptors: Comparative Analysis, Programming Languages, Data Analysis, Evaluation Methods
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