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Da Teng; Xiangyang Wang; Yanwei Xia; Yue Zhang; Lulu Tang; Qi Chen; Ruobing Zhang; Sujin Xie; Weiyong Yu – Education and Information Technologies, 2025
The swift advancement of artificial intelligence, especially large language models (LLMs), has generated novel prospects for improving educational methodologies. Nonetheless, the successful incorporation of these technologies into pedagogical methods, such as flipped classrooms, continues to pose a challenge. This study investigates the…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Flipped Classroom, Technology Uses in Education
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Carlos Sandoval-Medina; Carlos Argelio Arévalo-Mercado; Estela Lizbeth Muñoz-Andrade; Jaime Muñoz-Arteaga – Journal of Information Systems Education, 2024
Learning basic programming concepts in computer science-related fields poses a challenge for students, to the extent that it becomes an academic-social problem, resulting in high failure and dropout rates. Proposed solutions to the problem can be found in the literature, such as the development of new programming languages and environments, the…
Descriptors: Cognitive Ability, Computer Science Education, Programming, Instructional Materials
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Priti Oli; Rabin Banjade; Arun Balajiee Lekshmi Narayanan; Peter Brusilovsky; Vasile Rus – Grantee Submission, 2023
Self-efficacy, or the belief in one's ability to accomplish a task or achieve a goal, can significantly influence the effectiveness of various instructional methods to induce learning gains. The importance of self-efficacy is particularly pronounced in complex subjects like Computer Science, where students with high self-efficacy are more likely…
Descriptors: Computer Science Education, College Students, Self Efficacy, Programming
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Peiris, K. Dharini Amitha; Gallupe, R. Brent – Decision Sciences Journal of Innovative Education, 2018
Recommender-driven online learning systems (ROLS) are at the forefront of new computer-based learning. They incorporate machine learning to allow learning-by-doing, generating personalized recommendations in the process. This article describes the evaluations of a new type of online learning systems, ROLS. This evaluation was carried out in three…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming Languages, Conventional Instruction
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Troussas, Christos; Krouska, Akrivi; Sgouropoulou, Cleo – IEEE Transactions on Education, 2021
Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students' cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students' knowledge level to fuzzy weights, and using rule-based decision making, delivers…
Descriptors: Undergraduate Students, Intelligent Tutoring Systems, Computer Science Education, Programming
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Dawar, Deepak – Information Systems Education Journal, 2022
Learning computer programming is a challenging task for most beginners. Demotivation and learned helplessness are pretty common. A novel instructional technique that leverages the value-expectancy motivational model of student learning was conceptualized by the author to counter the lack of motivation in the introductory class. The result was a…
Descriptors: Teaching Methods, Introductory Courses, Computer Science Education, Assignments
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Tlili, Ahmed; Denden, Mouna; Essalmi, Fathi; Jemni, Mohamed; Chang, Maiga; Kinshuk; Chen, Nian-Shing – Interactive Learning Environments, 2023
The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…
Descriptors: Learning Analytics, Learning Management Systems, Intelligent Tutoring Systems, Bayesian Statistics
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Turkmen, Gamze; Caner, Sonay – Turkish Online Journal of Distance Education, 2020
This study aims to provide a comprehensive and in-depth investigation of the debugging process in programming teaching in terms of cognitive and metacognitive aspects, based on programming students who demonstrate low, medium, and high programming performance and to propose instructional strategies for scaffolding novice learners in an effective…
Descriptors: Programming, Novices, Electronic Learning, Troubleshooting
Green, Nicholas – ProQuest LLC, 2017
Worked-out examples are a common teaching strategy that aids learners in understanding concepts by use of step-by-step instruction. Literature has shown that they can be extremely beneficial, with a large body of material showing they can provide benefits over regular problem solving alone. This research looks into the viability of using this…
Descriptors: Computer Science Education, Intelligent Tutoring Systems, Teaching Methods, Units of Study
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Price, Thomas W.; Dong, Yihuan; Barnes, Tiffany – International Educational Data Mining Society, 2016
Intelligent Tutoring Systems (ITSs) have shown success in the domain of programming, in part by providing customized hints and feedback to students. However, many popular novice programming environments still lack these intelligent features. This is due in part to their use of open-ended programming assignments, which are difficult to support with…
Descriptors: Intelligent Tutoring Systems, Programming, Data, Computer Science Education
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Westerfield, Giles; Mitrovic, Antonija; Billinghurst, Mark – International Journal of Artificial Intelligence in Education, 2015
We investigate the combination of Augmented Reality (AR) with Intelligent Tutoring Systems (ITS) to assist with training for manual assembly tasks. Our approach combines AR graphics with adaptive guidance from the ITS to provide a more effective learning experience. We have developed a modular software framework for intelligent AR training…
Descriptors: Intelligent Tutoring Systems, Computer Simulation, Computer Science Education, Instructional Effectiveness
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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
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Lee, Nancy; Hong, Eunsook – IAFOR Journal of Education, 2017
The study described here explored the differential effects of two learning strategies, self-explanation and reading questions and answers, on learning the computer programming language JavaScript. Students' test performance and perceptions of effectiveness toward the two strategies were examined. An online interactive tutorial instruction…
Descriptors: Computer Science Education, Programming, Introductory Courses, High School Students
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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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Hsieh, Tung-Cheng; Lee, Ming-Che; Su, Chien-Yuan – Educational Technology & Society, 2013
In recent years, the demand for computer programming professionals has increased rapidly. These computer engineers not only play a key role in the national development of the computing and software industries, they also have a significant influence on the broader national knowledge industry. Therefore, one of the objectives of information…
Descriptors: Foreign Countries, Computer Science Education, Individualized Instruction, Remedial Instruction
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