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Showing 1 to 15 of 47 results Save | Export
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Cody, Christa; Maniktala, Mehak; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2022
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation…
Descriptors: Comparative Analysis, Prior Learning, Intelligent Tutoring Systems, Problem Solving
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Lodder, Josje; Heeren, Bastiaan; Jeuring, Johan – Journal of Computer Assisted Learning, 2019
This article describes an experiment with LogEx, an e-learning environment that supports students in learning how to prove the equivalence between two logical formulae, using standard equivalences such as DeMorgan. In the experiment, we compare two groups of students. The first group uses the complete learning environment, including hints, next…
Descriptors: Logical Thinking, Feedback (Response), Instructional Effectiveness, Intelligent Tutoring Systems
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Yung-Hsiang Hu; Jo Shan Fu; Hui-Chin Yeh – Interactive Learning Environments, 2024
Artificial intelligence aims to restructure and process re-engineering education and teaching processes and accelerate the evolution of the whole education system from information to intelligence. Robotic Process Automation (RPA) robots learn by observing people at work, analyzing user processes repeatedly, and adjusting or correcting automated…
Descriptors: Intelligent Tutoring Systems, Robotics, Automation, Instructional Effectiveness
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Katz, Sandra; Albacete, Patricia; Chounta, Irene-Angelica; Jordan, Pamela; McLaren, Bruce M.; Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…
Descriptors: Physics, Science Instruction, Pretests Posttests, Scores
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Fabic, Geela Venise Firmalo; Mitrovic, Antonija; Neshatian, Kourosh – International Journal of Artificial Intelligence in Education, 2019
The overarching goal of our project is to design effective learning activities for PyKinetic, a smartphone Python tutor. In this paper, we present a study using a variant of Parsons problems we designed for PyKinetic. Parsons problems contain randomized code which needs to be re-ordered to produce the desired effect. In our variant of Parsons…
Descriptors: Telecommunications, Handheld Devices, Cues, Intelligent Tutoring Systems
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Ibili, Emin; Çat, Mevlüt; Resnyansky, Dmitry; Sahin, Sami; Billinghurst, Mark – International Journal of Mathematical Education in Science and Technology, 2020
The aim of this research was to examine the effect of Augmented Reality (AR) supported geometry teaching on students' 3D thinking skills. This research consisted of 3 steps: (1) developing a 3D thinking ability scale, (ii) design and development of an AR Geometry Tutorial System (ARGTS) and (iii) implementation and assessment of geometry teaching…
Descriptors: Geometry, Thinking Skills, Mathematics Instruction, Pretests Posttests
Li, Haiying; Graesser, Art C. – Grantee Submission, 2020
This study investigated the impact of conversational agent formality on the quality of summaries and formality of written summaries during the training session and on posttest in a trialog-based intelligent tutoring system (ITS). During training, participants learned summarization strategies with the guidance of conversational agents who spoke one…
Descriptors: Intelligent Tutoring Systems, Writing Instruction, Writing Skills, Language Styles
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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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Wu, Huey-Min – Educational Psychology, 2019
Based on a cognitive diagnostic model, an online individualised tutor program was developed in this study. An experiment was conducted in practical educational settings exploring the effectiveness of the online individualised tutor remedial program based on the diagnostic reports of the cognitive diagnostic model. The methodology of this study was…
Descriptors: Mathematics Instruction, Intelligent Tutoring Systems, Instructional Effectiveness, Teaching Methods
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Zhou, Guojing; Wang, Jianxun; Lynch, Collin F.; Chi, Min – International Educational Data Mining Society, 2017
In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random…
Descriptors: Learning Theories, Teaching Methods, Decision Making, Intelligent Tutoring Systems
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McCarthy, Kathryn S.; Jacovina, Matthew E.; Snow, Erica L.; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2017
iSTART is an intelligent tutoring system designed to provide self-explanation instruction and practice to improve students' comprehension of complex, challenging text. This study examined the effects of extended game-based practice within the system as well as the effects of two metacognitive supports implemented within this practice. High school…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
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VanLehn, Kurt; Chung, Greg; Grover, Sachin; Madni, Ayesha; Wetzel, Jon – International Journal of Artificial Intelligence in Education, 2016
A common hypothesis is that students will more deeply understand dynamic systems and other complex phenomena if they construct computational models of them. Attempts to demonstrate the advantages of model construction have been stymied by the long time required for students to acquire skill in model construction. In order to make model…
Descriptors: Models, Science Instruction, Intelligent Tutoring Systems, Teaching Methods
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Hull, Alison; du Boulay, Benedict – Computer Science Education, 2015
Motivation and metacognition are strongly intertwined, with learners high in self-efficacy more likely to use a variety of self-regulatory learning strategies, as well as to persist longer on challenging tasks. The aim of the research was to improve the learner's focus on the process and experience of problem-solving while using an Intelligent…
Descriptors: Motivation, Metacognition, Feedback (Response), Intelligent Tutoring Systems
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Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2017
The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology…
Descriptors: Biology, Science Instruction, Intelligent Tutoring Systems, Probability
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Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – Grantee Submission, 2017
Collaborative and individual learning are both frequently used in classrooms to support learning. However, little research has investigated the benefits of combining individual and collaborative learning, as compared to learning only individually or only collaboratively. With our study, we address this research gap. We compared a combined…
Descriptors: Cooperative Learning, Grade 4, Grade 5, Elementary School Students
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