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Showing 1 to 15 of 18 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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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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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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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
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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
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
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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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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
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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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Backenköhler, Michael; Scherzinger, Felix; Singla, Adish; Wolf, Verena – International Educational Data Mining Society, 2018
Course selection can be a daunting task, especially for first year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a…
Descriptors: Data, Course Selection (Students), Information Utilization, Individualized Instruction
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Brita-Paja, J. L.; Gregorio, C.; Llana, L.; Pareja, C.; Riesco, A. – Interactive Learning Environments, 2019
During the last years online education, in particular Massive Open Online Courses (MOOCs), has contributed to spread and popularize educational methodologies such as peer-review, automatic assessment, self-paced courses, self-evaluation, etc. Although these techniques can benefit face-to-face courses, most of them are not yet widely used in these…
Descriptors: Online Courses, Synchronous Communication, Undergraduate Study, Peer Evaluation
Mok, Heng Ngee – Online Submission, 2011
Differentiated instruction in the form of tiered take-home lab exercises was implemented for students of an undergraduate-level programming course. This paper attempts to uncover the perceptions and usage patterns of students toward these new lab exercises using a comprehensive survey. Findings reveal that these tiered exercises are generally very…
Descriptors: Learner Engagement, Cooperative Learning, Programming, Individualized Instruction
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Lee, Jae-Shin; Cho, Hichang – Educational Technology & Society, 2011
The purpose of this study was to identify and analyze the processes of seeking information online and evaluating this information. We hypothesized that individuals' social network, in-out group categorization, and cultural proclivity would influence their online information-seeking behavior. Also, we tested whether individuals differentiated…
Descriptors: Network Analysis, Information Sources, Information Seeking, Foreign Countries
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Yang, Tzu-Chi; Hwang, Gwo-Jen; Yang, Stephen Jen-Hwa – Educational Technology & Society, 2013
In this study, an adaptive learning system is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive learning systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's learning style. An…
Descriptors: Cognitive Style, Experimental Groups, Control Groups, Cognitive Ability
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