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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
Peidi Gu; Zui Cheng; Cheng Miaoting; John Poggio; Yan Dong – Journal of Computer Assisted Learning, 2025
Background: Today, the importance of STEM (Science, Technology, Engineering and Mathematics) education and training is widely recognised and accepted. Computer programming courses have become essential in higher education to nurture students' programming, analysis and computational skills, which are vital for success in all STEM fields and areas.…
Descriptors: Active Learning, Student Projects, Individualized Instruction, Student Motivation
O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
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
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
Karaoglan Yilmaz, Fatma Gizem; Yilmaz, Ramazan – Technology, Knowledge and Learning, 2020
There is a growing interest in the use of learning analytics in higher education institutions. Learning analytics also appear to have the potential to be used to provide personalized feedback and support in online learning. However, when the literature is examined, the use of learning analytics for this purpose appears as a gap to be investigated.…
Descriptors: Student Attitudes, Individualized Instruction, Electronic Learning, Feedback (Response)
Lokkila, Erno; Christopoulos, Athanasios; Laakso, Mikko-Jussi – Journal of Information Systems Education, 2023
Educators who teach programming subjects are often wondering "which programming language should I teach first?" The debate behind this question has a long history and coming up with a definite answer to this question would be farfetched. Nonetheless, several efforts can be identified in the literature wherein pros and cons of mainstream…
Descriptors: Comparative Analysis, Programming Languages, Probability, Error Patterns
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
Moallem, Mahnaz; Loksa, Dastyni C.; Vandiver, Marcia; Li, Qing; Cai, Qijie; Billman, Rachel; Christenson, Lea Ann; Kara, Melike; Engbert, Christine – International Association for Development of the Information Society, 2022
The paper shares the results of the process of forming a cross-disciplinary collaborative team and using a user-centered design framework to co-create a self-directed, self-paced, personalized, flexible learning environment as an alternative approach to preparing PK-8 preservice teachers to teach computational thinking. It is part of a larger…
Descriptors: Computation, Thinking Skills, Computer Science Education, Instructional Design
Yuli Deng – ProQuest LLC, 2021
Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of…
Descriptors: Individualized Instruction, Experiential Learning, Computer Science Education, Electronic Learning
Aziman Abdullah – International Society for Technology, Education, and Science, 2023
This study explores the potential of using screen time data in learning management systems (LMS) to estimate student learning time (SLT) and validate the credit value of courses. Gathering comprehensive data on actual student learning time is difficult, so this study uses LMS Moodle logs from a computer programming course with 490 students over 16…
Descriptors: Time Factors (Learning), Handheld Devices, Computer Use, Television Viewing
Luan, Hui; Tsai, Chin-Chung – Educational Technology & Society, 2021
In recent years, in the field of education, there has been a clear progressive trend toward precision education. As a rapidly evolving AI technique, machine learning is viewed as an important means to realize it. In this paper, we systematically review 40 empirical studies regarding machine-learning-based precision education. The results showed…
Descriptors: Artificial Intelligence, Individualized Instruction, Individual Differences, Educational Trends
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
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
Kleinman, Erica; Shergadwala, Murtuza N.; Teng, Zhaoqing; Villareale, Jennifer; Bryant, Andy; Zhu, Jichen; Seif El-Nasr, Magy – Journal of Learning Analytics, 2022
Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students' problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret…
Descriptors: Problem Solving, Learning Analytics, Decision Making, Educational Technology