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Design of a Personalised Adaptive Ubiquitous Learning System
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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
Artificial Intelligence-Supported Art Education: A Deep Learning-Based System for Promoting University Students' Artwork Appreciation and Painting Outcomes
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Min-Chi Chiu; Gwo-Jen Hwang; Lu-Ho Hsia; Fong-Ming Shyu – Interactive Learning Environments, 2024
In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this…
Descriptors: Art Education, Artificial Intelligence, Teaching Methods, Comparative Analysis
Using Supervised Machine Learning on Large-Scale Online Forums to Classify Course-Related Facebook Messages in Predicting Learning Achievement within the Personal Learning Environment
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Wu, Jiun-Yu; Hsiao, Yi-Cheng; Nian, Mei-Wen – Interactive Learning Environments, 2020
This paper demonstrated the use of the supervised Machine Learning (ML) for text classification to predict students' final course grades in a hybrid Advanced Statistics course and exhibited the potential of using ML classified messages to identify students at risk of course failure. We built three classification models with training data of 76,936…
Descriptors: Social Media, Discussion Groups, Artificial Intelligence, Classification
Predicting Students' Academic Performance by Using Educational Big Data and Learning Analytics: Evaluation of Classification Methods and Learning Logs
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Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
Differences between Students' Learning Behaviors and Performances of Adopting a Competitive Game-Based Item Bank Practice Approach for Learning Procedural and Declarative Knowledge
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Hwang, Gwo-Haur; Chen, Beyin; Chen, Ru-Shan; Wu, Ting-Ting; Lai, Yu-Ling – Interactive Learning Environments, 2019
Competitive game-based learning has been widely discussed in terms of its positive and negative impacts on learners' learning effectiveness and learning behavior. Although different types of games require different kinds of knowledge to accomplish the task via competition, few studies have considered that knowledge types, such as procedural…
Descriptors: Student Behavior, Adoption (Ideas), Competition, Game Based Learning
Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment
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Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
Applying Social Tagging to Manage Cognitive Load in a Web 2.0 Self-Learning Environment
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Huang, Yueh-Min; Huang, Yong-Ming; Liu, Chien-Hung; Tsai, Chin-Chung – Interactive Learning Environments, 2013
Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is…
Descriptors: Web 2.0 Technologies, Cognitive Processes, Difficulty Level, College Students