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Dutt, Sarthika; Ahuja, Neelu Jyothi; Kumar, Manoj – Education and Information Technologies, 2022
Several studies have investigated the need for learning difficulties identification specifically Dyslexia, Dysgraphia and Dyscalculia. The identification of these difficulties among children is a multiple screening process under psychologist's supervision. Learning difficulties identification is a difficult task; it affects the learning process…
Descriptors: Intelligent Tutoring Systems, Special Education, Learning Disabilities, Students with Disabilities
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Chango, Wilson; Cerezo, Rebeca; Sanchez-Santillan, Miguel; Azevedo, Roger; Romero, Cristóbal – Journal of Computing in Higher Education, 2021
The aim of this study was to predict university students' learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions,…
Descriptors: Grade Prediction, Intelligent Tutoring Systems, College Students, Data Use
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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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Hannah Smith; Avery H. Closser; Erin Ottmar; Jenny Yun-Chen Chan – Applied Cognitive Psychology, 2022
Worked examples are effective learning tools for algebraic equation solving. However, they are typically presented in a static concise format, which only displays the major derivation steps in one static image. The current work explores how worked examples that vary in their extensiveness (i.e., detail) and degree of dynamic presentation (i.e.,…
Descriptors: Algebra, Mathematics Instruction, Equations (Mathematics), 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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Wang, Dongqing; Han, Hou – Journal of Computer Assisted Learning, 2021
With the development of a technology-supported environment, it is plausible to provide rich process-oriented feedback in a timely manner. In this paper, we developed a learning analytics dashboard (LAD) based on process-oriented feedback in iTutor to offer learners their final scores, sub-scale reports, and corresponding suggestions on further…
Descriptors: Learning Analytics, Educational Technology, Feedback (Response), Intelligent Tutoring Systems
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Harati, Hoda; Sujo-Montes, Laura; Tu, Chih-Hsiung; Armfield, Shadow J. W.; Yen, Cherng-Jyh – Education Sciences, 2021
Adaptive learning is an educational method that uses computer algorithms and artificial intelligence (AI) to customize learning materials and activities based on each user's model. Adaptive learning has been used for more than 20 years. However, it is still unique, and no other system could bring more or even similar capabilities than the ones…
Descriptors: Intelligent Tutoring Systems, Questionnaires, Self Management, Learning Strategies
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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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Sales, Adam C.; Pane, John F. – International Educational Data Mining Society, 2020
The design of the Cognitive Tutor Algebra I (CTA1) intelligent tutoring system assumes that students work through sections of material following a pre-specified order, and only move on from one section to the next after mastering the first section's skills. However, the software gives teachers the flexibility to override that structure, by…
Descriptors: Student Placement, Intelligent Tutoring Systems, Algebra, Mathematics Instruction
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Sales, Adam; Prihar, Ethan; Heffernan, Neil; Pane, John F. – International Educational Data Mining Society, 2021
This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect…
Descriptors: Intelligent Tutoring Systems, Academic Achievement, Educational Technology, Algebra
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How, Meng-Leong; Hung, Wei Loong David – Education Sciences, 2019
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved…
Descriptors: Stakeholders, Artificial Intelligence, Bayesian Statistics, Probability
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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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Demir, Ümit – Pedagogies: An International Journal, 2020
This research was conducted to determine the effects of the using negative knowledge-based Intelligent Teaching System (ITS) evaluator software in the assessment and evaluation processes of English Teaching as a Foreign language. Experimental design with the pretest-posttest control group was used in this study. The study group consists of 67…
Descriptors: Misconceptions, Intelligent Tutoring Systems, Computer Software, Vocational Education
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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
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