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Showing 1 to 15 of 40 results Save | Export
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Mengning Mu; Man Yuan – Interactive Learning Environments, 2024
The necessity for students to clarify their own cognitive structure and the amount of their knowledge mastery for self-reflection is often ignored in building the student model in the adaptive model, which makes the construction of the cognitive structure pointless. Simultaneously, knowledge forgetting causes students' knowledge level to fall…
Descriptors: Individualized Instruction, Cognitive Processes, Graphs, Cognitive Structures
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Tri Puspa Rinjeni; Nur Aini Rakhmawati; Reny Nadlifatin – Journal of Information Technology Education: Research, 2024
Aim/Purpose: This study identifies gamification element preferences based on Myers-Briggs Type Indicator (MBTI) characteristics. It measures the influence of preferences on learning motivation through a pre-experimental design of one group pre-test post-test. Background: Incorporating information technology in education has led to the introduction…
Descriptors: Foreign Countries, Personality Measures, College Students, Information Systems
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Nour Eddine El Fezazi; Smaili El Miloud; Ilham Oumaira; Mohamed Daoudi – Educational Process: International Journal, 2025
Background/purpose: Mobile learning (M-learning) has become a crucial component of higher education due to the increasing demand for flexible and adaptive learning environments. However, ensuring personalized and effective M-learning experiences remains a challenge. This study aims to enhance M-learning effectiveness by introducing an AI-driven…
Descriptors: Electronic Learning, Learning Management Systems, Instructional Effectiveness, Artificial Intelligence
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Rani Van Schoors; Sohum M. Bhatt; Jan Elen; Annelies Raes; Wim Van den Noortgate; Fien Depaepe – International Journal of Designs for Learning, 2024
Due to swift technological changes in society, programming tasks are proliferating in formal and informal education around the globe. However, challenges arise regarding the acquisition of programming skills. Many students are unequipped to develop programming skills due to limited instruction or background and therefore feel insecure when…
Descriptors: Secondary School Students, Grade 1, Individualized Instruction, Electronic Learning
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Ibrahim Abba Mohammed; Ahmed Bello; Bala Ayuba – Education and Information Technologies, 2025
In spite of the emergence of studies seeking to integrate chatbot into education, there is a wide literature gap in the Nigerian contexts. While most studies focus on the design and development of chatbots, there exists a very scarce literature on the effect of ChatGPT chatbot on students' achievement. To address this gap, this study checked the…
Descriptors: Natural Language Processing, Artificial Intelligence, Academic Achievement, Computer Science Education
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Hajar Majjate; Youssra Bellarhmouch; Adil Jeghal; Ali Yahyaouy; Hamid Tairi; Khalid Alaoui Zidani – Education and Information Technologies, 2025
In recent times, there has been a growing interest in enhancing recommendation systems for e-learning platforms to deliver a personalised learning experience that meets each learner's distinct requirements and preferences. Nevertheless, it is crucial to recognise the ethical considerations surrounding this technology, as it heavily relies on…
Descriptors: Ethics, Artificial Intelligence, Technology Uses in Education, Student Attitudes
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Al-Malki, Laila; Meccawy, Maram – Computers in the Schools, 2022
In this study, a personalized gamified recommender system was developed to help secondary-school students in Saudi Arabia learn computer programming. This recommender system supports those students by providing personalized recommendations to address their weaknesses and increase their motivation toward computer programming. A total of 60 female…
Descriptors: Academic Achievement, Student Motivation, Computer Science Education, Programming
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Sonia Triana-Vera; Omar López-Vargas – Contemporary Educational Technology, 2025
This research aimed to determine the effects of motivational scaffolding and adaptive scaffolding on academic and online self-efficacy in learners interacting with a multimedia learning environment within the field of technology. The study involved 146 students from four tenth-grade classes at a public institution in the municipality of Soacha…
Descriptors: Self Efficacy, Electronic Learning, Scaffolding (Teaching Technique), High School Students
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Krenare Pireva Nuci – Educational Process: International Journal, 2025
Background/purpose: With advancements in technology, particularly in Artificial Intelligence (AI), personalized and adaptive systems are increasingly being integrated into conventional educational environments. These technologies create opportunities to place learners at the center of the educational experience through personalized learning.…
Descriptors: Technology Uses in Education, Artificial Intelligence, Educational Technology, Electronic Learning
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Mehmet Firat; Saniye Kuleli – Journal of Educational Technology and Online Learning, 2024
This research investigates the comparative effectiveness of the ChatGPT and the Google search engine in facilitating the self-learning of JavaScript functions among undergraduate open and distance learning students. The study employed a quasi-experimental post-test control group design to analyze the variables of disorientation, satisfaction,…
Descriptors: Comparative Analysis, Web Sites, Computer Software, Artificial Intelligence
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Ajay Kumar Yadav; Dil Prasad Shrestha – International Journal of Computer Science Education in Schools, 2025
This study analyses the Computer Science (CS) curricula for grades 9 and 10 in Nepal, emphasising students' interests and needs within the social context. The study applied the mixed-methods research design. The quantitative data were collected from questionnaire surveys with students and teachers. The qualitative information was collected from…
Descriptors: Computer Science Education, Curriculum Development, Secondary School Students, Student Interests
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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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Fonseca, Samuel C.; Pereira, Filipe Dwan; Oliveira, Elaine H. T.; Oliveira, David B. F.; Carvalho, Leandro S. G.; Cristea, Alexandra I. – International Educational Data Mining Society, 2020
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for…
Descriptors: Programming, Introductory Courses, Computer Science Education, Automation
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Alshammari, Mohammad T.; Qtaish, Amjad – Journal of Information Technology Education: Research, 2019
Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its…
Descriptors: Electronic Learning, Cognitive Style, Knowledge Level, Individualized Instruction
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Hariyanto, Didik; Triyono, Moch. Bruri; Köhler, Thomas – Knowledge Management & E-Learning, 2020
One of the advanced technologies in e-learning deals with the systems' ability to fit the students' preferences. It emerged based upon the common conception that every person has different learning style. However, despite the many options of learning style models toward using personalized elearning, there are considerable challenges to assess the…
Descriptors: Usability, Electronic Learning, Individualized Instruction, Computer Assisted Instruction
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