• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1841-1848
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• 1999

DELVAUX is a genetics-based inductive learning system that learns a rule-set, which consists of Bayesian classification rules, from sets of examples for classification tasks. One problem that DELVAUX faces in the rule-set learning process is that, occasionally, the learning process ends with a local optimum without finding the best rule-set. Another problem is that, occasionally, the learning process ends with a rule-set that performs well for the training examples but not for the unknown testing examples. This paper describes efforts to alleviate these two problems centering on the N-version learning approach, in which multiple rule-sets are learning and a classification system is constructed with those learned rule-sets to improve the overall performance of a classification system. For the implementation of the N-version learning approach, we propose a decision-making scheme that can draw a decision using multiple rule-sets and a genetic algorithm approach to find a good combination of rule-sets from a set of learned rule-sets. We also present empirical results that evaluate the effect of the N-version learning approach in the DELVAUX learning environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• Educational Technology International
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• v.19 no.2
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• pp.175-197
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• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• Journal for History of Mathematics
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• v.28 no.6
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• pp.333-348
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• 2015

Problem sovling skill is one of the core skills in mathematics education. To improve students' problem solving skill, the project approach or project based learning has been developed and applied. A teaching and learning strategy utilizing 'project' encourages students to understand the problem embedded in the project, find and reflect the solution, which might be effective in improving students' problem solving skill. The present study systematically reviews literature regarding project based learning and analyzes the characteristics of project. The findings from the systematic review illuminate an appropriate approach to apply project based learning in mathematics classrooms.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.330-338
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• 2017

The purpose of this study is to examine the effect of blended learning approach on academic achievement and self directed learning skills of nursing undergraduate students. This experimental study is designed for a nonequivalent control group. The number of subjects in this research consists of 145, where 75 of the experimental group participated in blended learning program and 70 of the control group didn't do. The data was analyzed by $x^2$-test, Fisher's exact test, t-test and paired t-test. The effects of blended learning approach on learning outcomes in women's health nursing are as follows : The academic achievement of the experimental group has been more elevated than that of the control group on final examination. The experimental group has made increase in self directed learning skills. The blended learning approach on learning outcomes in nursing major are effective. This study has significant implication in that it identified the availability of the blended learning program and that it would be a useful teaching and learning method to achieve learning outcomes.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.268-281
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• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.

• Journal of Korean Elementary Science Education
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• v.34 no.2
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• pp.252-263
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• 2015

This study analyzed the effects of science outdoor activity applying a Brain-Based Evolutionary (ABC-DEF) approach on elementary school students' interest and academic achievement. Samples of the study were composed of 3 classes of 67 sixth graders in Seoul, Korea. Unit of 'Ecosystem and Environment' was selected as a object of the research. Textbook- and teachers' guidebook-based instruction was implemented in comparison group, brain-based evolutionary approach within classroom in experimental group A, and science outdoor learning by a brain-based evolutionary approach in experimental group B. In order to analyze the quantitative differences of students' interests and achievements, three tests of 'General Science Attitudes', 'Applied Unit-Related Interests', and 'Applied Unit-Related Achievement' were administered to the students. To find out the characteristics which would not be apparently revealed by quantitative tests, qualitative data such as portfolios, daily records of classroom work, and interview were also analyzed. The major results of the study are as follows. First, for post-test of interest, a statistically significant difference between comparison group and experimental group B was found. Especially, the 'interests about biology learning' factor, when analyzed by each item, was significant in two questions. Results of interviews the students showed that whether the presence or absence of outdoor learning experience influenced most on their interests about the topic. Second, for post-test of achievement, the difference among 3 groups according to high, middle, and low levels of post-interest was not statistically significant, but the groups of higher scores in post-interest tends to have higher scores in post-achievement. It can be inferred that outdoor learning by a brain-based evolutionary approach increases students' situational interests about leaning topic. On the basis of the results, the implications for the research in science education and the teaching and learning in school are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.614-621
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• 2006

We have implemented a multiclassifier learning approach in a GA-based inductive learning environment that learns classification rules that are similar to rules used in PROSPECTOR. In the multiclassifier learning approach, a classification system is constructed with several classifiers that are obtained by running a GA-based learning system several times to improve the overall performance of a classification system. To implement the multiclassifier learning approach, we need a decision-making scheme that can draw a decision using multiple classifiers. In this paper, we introduce two decision-making schemes: one is based on combining posterior odds given by classifiers to each class and the other one is a voting scheme based on ranking assigned to each class by classifiers. We also present empirical results that evaluate the effect of the multiclassifier learning approach on the GA-based inductive teaming environment.