• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.209-214
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• 2024

This is an extended research paper focusing on the applications of Machine Learing and Artificial Intelligence in virtual learning environment. The world is moving at a fast pace having the application of Machine Learning (ML) and Artificial Intelligence (AI) in all the major disciplines and the educational sector is also not untouched by its impact especially in an online learning environment. This paper attempts to elaborate on the benefits of ML and AI in E-Learning (EL) in general and explain how King Khalid University (KKU) EL Deanship is making the best of ML and AI in its practices. Also, researchers have focused on the future of ML and AI in any academic program. This research is descriptive in nature; results are based on qualitative analysis done through tools and techniques of EL applied in KKU as an example but the same modus operandi can be implemented by any institution in its EL platform. KKU is using Learning Management Services (LMS) for providing online learning practices and Blackboard (BB) for sharing online learning resources, therefore these tools are considered by the researchers for explaining the results of ML and AI.

• Journal of Digital Convergence
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• v.7 no.1
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• pp.137-147
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• 2009

The main purpose of cyber home education in public sector is to promote public education and restrain expensive private education expenses. The primary purpose of this study is to explore the effects of motivation, immersion and tutor on the effectiveness of cyber home learning. The variables of motivation, immersion and tutor participation were incorporated in this study as follows: 1) tutor participation was classed as a single independent variable, which has interaction and also effects passion; 2) motivation and 3) immersion were classed as two mediatory variables: motivation which include relevance and confidence; and immersion which includes attention and controllability. 4) learning effect was classed as a single dependent variable of learning influence factor which has learning attitude and learning satisfaction. The results show that a tutor had a direct influence on the effects of the learners' study but motivation and immersion had an indirect influence on the effects of learners' study independently. Based on these findings a tutor should provide motivation and immersion with various learning methods and contents for learners to get maximum benefits from cyber home learning.

• School Mathematics
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• v.4 no.2
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• pp.237-246
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• 2002

The purpose of this paper is to investigatethe relationship between Korean elementary teachers' teaching practice and students' perceptions of classroom learning environment. It is important to examine whether teachers conceive of mathematics and their teaching practice are consistent with their actual teaching mathematics in classroom. To do so, two reform-oriented and two traditionally-oriented elementary teachers in Seoul and a total of 130 students from these four selected teachers' classes participated in the present study Using classroom environment survey scale [CES] the differences in students' perceptions on classroom learning environment were examined. The results show that students taught by reform-oriented teachers have significantly higher score in the area of participation, relevance, and commitment to learning than traditionally-oriented group of students. Thus, we may conclude that the reform-oriented group of students have more benefits in learning mathematics than do the traditionally-oriented group of students.

• East Asian Economic Review
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• v.19 no.3
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• pp.243-273
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• 2015

The purpose of this paper is to identify learning in games in experimental economic settings, and apply their results to real multilateral trade negotiations, such as the Doha Development Agenda (DDA) in the World Trade Organizations (WTO). This paper argues that the structure of games including a veto player (Veto games) is similar to the WTO/DDA negotiations in that the players do not possess identical power. This paper's main contribution to the literature involves showing that learning about power is dominant over learning from simple repetition in Veto games. Additionally, this paper shows that players are concerned about how much they have gained in previous games in Veto games, although their memories generally do not last beyond the next game, and thus they tend to be selfish as they have less shares. Based on these results, there is a possibility to be more generous in the distribution of benefits by allowing players without veto power to retain special rights so that they would not be totally powerless. It also shows the necessity of having "respite" in the process of negotiations and policy options for choosing partners for winning coalitions.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.152-163
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• 2023

This research aimed at investigating Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils. It also aimed at determining the statistical differences among variables due to sex, educational qualification, number of years of experience, and training sessions. This research adopted the descriptive approach. The sample consisted of male and female primary teachers of Islamic Studies (N=250) in governmental schools in Taif. The questionnaire was used as a main research tool. It included (20) items. Results showed that Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils were ranked to a medium degree. There was a statistically significant difference among primary Islamic Studies teachers' attitudes due to sex variable. It was recommended to adopt more training sessions and seminars for adopting the idea of utilizing virtual learning environments among Islamic Studies teachers at boys' and girls' school in Mecca through emphasizing its significance and benefits in Teaching.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.77-84
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• 2009

Along with developments of information and communication technologies, internet has spread not only all over the society, but also our everyday life deeply. Also requirements for e-learning using internet in the educational aspect have a great influence on the changes of school educations. The benefits of e-learning are many, including cost-effectiveness, enhanced responsiveness to change, consistency, and timely contents. Therefore, the e-learning has been introduced to the universities. However, the e-learning is operated inefficiently because of introduction to the university with no definite idea about effects of education and economy in the university. Therefore, in this paper we analysed the category of e-learning based the curriculum operation forms in the university, surveyed tests about students preference and the studied what is desirable e-learning operation forms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1171-1174
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• 1993

A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.

• East Asian mathematical journal
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• v.35 no.2
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• pp.217-237
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• 2019

This Modeling is a cyclical process of creating and modifying models of empirical situations to understand them better and improve decisions. The role of modeling and teaching mathematical modeling in school mathematics has received increasing attention as generating authentic learning and revealing the ways of thinking that produced it. In this paper and interactive lecture session, we will review a subset of the related literature, discuss benefits and challenges in teaching and learning mathematical modeling, and share our attempts to improve traditional textbook problems so that they can become more authentic modeling activities and implications for instruction and assessment as well as for research.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.526-527
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• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing multi-modal speech emotion recognition system, we can get numerous benefits. This paper explain about fusing BERT as the text recognizer and CNN as the speech recognizer to built a multi-modal SER system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.