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Showing 1 to 15 of 32 results Save | Export
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Mongkhol Prasertsang; Metta Marwiang; Putcharee Junpeng – Journal of Education and Learning, 2025
This study aimed to assess and compare the development of mathematical procedures on the part of Grade 7 students using an intelligent tutoring system on a digital platform. The sample comprised 96 students from Khon Kaen University Demonstration School, Thailand, divided equally into experimental and control groups. The experimental group worked…
Descriptors: Foreign Countries, Mathematics Skills, Grade 7, Mathematics Instruction
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Metta Marwiang; Mongkhol Prasertsang; Putcharee Junpeng – Journal of Education and Learning, 2025
This study examined the effectiveness of an intelligent tutoring system (ITS) driven by real-time feedback in enhancing students' mathematical learning outcomes, as defined by the Structure of Observed Learning Outcomes. Conducted within the domains of Measurement and Geometry, the study employed a randomized controlled trial involving 120…
Descriptors: Mathematics Achievement, Mathematics Education, Intelligent Tutoring Systems, Feedback (Response)
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Nelekar, Shreeya; Abdulrahman, Amal; Gupta, Manik; Richards, Deborah – British Journal of Educational Technology, 2022
Stress has become one of the major reasons for many mental health related issues among students of all age groups, which has resulted in devastating personal losses including suicide. Societal and familial pressure to succeed is high, particularly in developing countries where education is highly valued as a key enabler. As part of stress…
Descriptors: Intelligent Tutoring Systems, Anxiety, Foreign Countries, College Students
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Muhammad Younas; Iskander Ismayil; Dina Abdel Salam El-Dakhs; Behzad Anwar – Open Praxis, 2025
This meta-analysis examines the diverse effects of artificial intelligence (AI), notably ChatGPT, on intelligent learning in the education industry over the last four years. Despite the rapid integration into education of AI tools such as ChatGPT, which have the potential to enhance personalized learning and administrative efficiency, there…
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Program Effectiveness
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Shih, Shu-Chuan; Chang, Chih-Chia; Kuo, Bor-Chen; Huang, Yu-Han – Education and Information Technologies, 2023
A one-on-one dialogue-based mathematics intelligent tutoring system (ITS) for learning multiplication and division of fractions was developed and evaluated in this study. This system could identify students' error types and misconceptions in real-time by using a block-based matching method. The adaptive dialogue-based instruction was supported by…
Descriptors: Mathematics Instruction, Intelligent Tutoring Systems, Multiplication, Division
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Çetin, Ismail; Erumit, Ali Kürsat; Nabiyev, Vasif; Karal, Hasan; Kosa, Temel; Kokoc, Mehmet – Participatory Educational Research, 2023
This study aims to examine the contribution of ArtiBos, which is designed as a Gamified Adaptive Intelligent Tutoring System for students' problem-solving skills. In the study, first of all, the system's design features to improve problem-solving skills were examined, and then the effect of the system on problem-solving skills was evaluated. The…
Descriptors: Gamification, Intelligent Tutoring Systems, Problem Solving, High School Students
Graesser, Arthur C.; Greenberg, Daphne; Frijters, Jan C.; Talwar, Amani – Grantee Submission, 2021
A large percentage of adults throughout the world have low reading skills. Computer technologies can potentially help these adults improve their literacy in addition to instructors at literacy centers. AutoTutor was designed to teach comprehension strategies by implementing conversational "trialogues" in which two computer agents (tutor…
Descriptors: Reading Achievement, Learner Engagement, Reading Comprehension, Intervention
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Chrysafiadi, Konstantina; Virvou, Maria; Tsihrintzis, George A.; Hatzilygeroudis, Ioannis – Education and Information Technologies, 2023
Nowadays, the improvement of digital learning with Artificial Intelligence has attracted a lot of research, as it provides solutions for individualized education styles which are independent of place and time. This is particularly the case for computer science, as a tutoring domain, which is rapidly growing and changing and as such, learners need…
Descriptors: Foreign Countries, Undergraduate Students, Computer Science Education, Programming
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Oliveira, Eduardo; de Barba, Paula; Corrin, Linda – Australasian Journal of Educational Technology, 2021
Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of…
Descriptors: Educational Technology, Technology Uses in Education, Artificial Intelligence, Undergraduate Students
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Dizon, Gilbert – Language Learning & Technology, 2020
While the use of intelligent personal assistants (IPAs) has exploded in recent years, little is known about their use to promote English as a foreign language (EFL) development. Thus, this study addresses this gap in the literature by examining the in-class use of the IPA, Alexa, among second language (L2) English students to support improvements…
Descriptors: Intelligent Tutoring Systems, Educational Technology, English (Second Language), Second Language Instruction
Fang, Ying; Lippert, Anne; Cai, Zhiqiang; Chen, Su; Frijters, Jan C.; Greenberg, Daphne; Graesser, Arthur C. – Grantee Submission, 2021
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Reading Comprehension
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Nygren, Eeva; Blignaut, A. Seugnet; Leendertz, Verona; Sutinen, Erkki – Informatics in Education, 2019
Technology-enhanced learning generally focuses on the cognitive rather than the affective domain of learning. This multi-method evaluation of the INBECOM project (Integrating Behaviourism and Constructivism in Mathematics) was conducted from the point of view of affective learning levels of Krathwohl "et al." (1964). The research…
Descriptors: Game Based Learning, Electronic Learning, Mathematics Instruction, Intelligent Tutoring Systems
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Aguerrebere, Cecilia; Cobo, Cristóbal; Whitehill, Jacob – International Educational Data Mining Society, 2018
When implementing large-scale educational computing initiatives (e.g., One Laptop Per Child) it is vital to allocate resources for training, support, and device deployment judiciously. One question that arises is how learners' engagement with online educational resources is affected by receiving a new computer; do the benefits justify the costs?…
Descriptors: Management Systems, Foreign Countries, Resource Allocation, Learner Engagement
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Shi, Genghu; Pavlik, Philip, Jr.; Graesser, Arthur – Grantee Submission, 2017
After developing an intelligent tutoring system (ITS), or any other class of learning environments, one of the first questions that should be asked is whether the system was effective in helping students learn the targeted skills or subject matter. In this study, we employed two educational data mining models (Additive Factor Model, AFM and…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Program Effectiveness
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Lenhard, Wolfgang; Baier, Herbert; Endlich, Darius; Schneider, Wolfgang; Hoffmann, Joachim – Journal of Research in Reading, 2013
There are many established reading strategy training programmes, which explicitly teach strategic and meta-cognitive knowledge to improve reading comprehension. Although instruction in strategy knowledge leads to improvements in meta-cognitive skills, the effects do not always transfer to reading comprehension. Therefore, to investigate…
Descriptors: Reading Instruction, Reading Strategies, Metacognition, Reading Comprehension
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