• Steel and Composite Structures
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• v.22 no.4
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• pp.733-744
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• 2016

In this study, optimum structural designs of braced (non-swaying) planar steel frames are investigated by using one of the recent meta-heuristic search techniques, teaching-learning based optimization. Optimum design problems are performed according to American Institute of Steel Construction- Allowable Stress Design (AISC-ASD) specifications. A computer program is developed in MATLAB interacting with SAP2000 OAPI (Open Application Programming Interface) to conduct optimization procedures. Optimum cross sections are selected from a specified list of 128W profiles taken from AISC. Two different braced planar frames taken from literature are carried out for stress, geometric size, displacement and inter-storey drift constraints. It is concluded that teaching-learning based optimization presents robust and applicable optimum solutions in multi-element structural problems.

• Research in Mathematical Education
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• v.22 no.1
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• pp.35-46
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• 2019

This study examines how open-ended tasks can be implemented with the support of redefined learning goals and teaching practices from a student-centered perspective. In order to apply open-ended tasks, learning goals should be adopted by individual student's cognitive levels in the classroom context rather than by designated goals from curriculum. Equitable opportunities to share children's mathematical ideas are also attainable through flexible management of lesson-time. Eventually, students can foster their meta-cognition in the process of abstraction of what they've learned through discussions facilitated by teachers. A pedagogical implication for professional development is that teachers need to improve additional teaching practices such as how to tailor tasks relevant to their classroom context and how to set norms for students to appreciate peer's mathematical ideas in the discussions.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.339-348
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• 2005

The trend of the computer education is shifting from problem-solving in the real world and using functions for it to behavioristic perspectives which encourage people to acquire the functions simply according to the applying programs of well-known companies. Thus, this research studies the computer instructional model which emphasizes the basic computer education and connections to the real world at the same time. In the constructivistic perspectives, this model emphasizes the learners activities, their using of meta-cognitive strategies to reflect their level of the lesson and collaborative-reflective learning of problem-solving. The research applied in the real computer skill education and according to the result of the research, I could find the experimental group got high level of learning achievement and this benefits to the high level group rather than low and middle group.

• The Journal of Korean Association of Computer Education
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• v.22 no.5
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• pp.1-9
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• 2019

A physical computing education is helpful for enhancing learners' computational thinking, creativity, and collaborative problem solving ability and so on. Recently, it is being actively promoted according to the software education policy and the 2015 revised national curriculum in Korea. This study describes a meta-synthesis of research on physical learning education that investigates the extent to which there is evidence of benefits and challenges for physical computing education. 37 articles were identified, and 20 articles met the inclusion criteria. The synthesis resulted in the list of purposes, teaching and learning methods, and physical computing tools, and benefits of physical computing education.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.58-65
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• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

• The Mathematical Education
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• v.50 no.1
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• pp.103-118
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• 2011

The study examined whether the relation between mathematics self-efficacy and mathematics achievement was partially mediated by the learning strategies, using latent growth model analyses. It was also examined the auto-regressive, cross-lagged (ARCL) panel model for testing the stability and change in the relation of mathematics self-efficacy and learning strategy over time. The study analyzed the first-year to the third-year data of the Korean Educational Longitudinal Survey (KELS). The result of ARCL panel model analysis showed that earlier mathematics self-efficacy could predict later learning strategy use. There were linear trends in mathematics self-efficacy, learning strategy, and mathematics achievement. Specifically, mathematics achievement was increased over the three time points, whereas mathematics self-efficacy and learning strategies were significantly decreased. In the analyses of latent growth models, the mediating effects of learning strategies were overall supported. That is, both of initial status and change rate of rehearsal strategy partially mediated the relation of mathematics self-efficacy and mathematics achievement. However, in elaboration and meta-cognitive strategies, only the initial status of each variable showed the indirect relationship.

• Education of Primary School Mathematics
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• v.22 no.4
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• pp.223-237
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• 2019

There is a close interaction between the linguistic-syntactic representation system and the affective representation system that appear in the mathematical process. On the other hand, since the mathematical conceptual system is fundamentally metaphoric, the analysis of the mathematical concept structure through linguistic representation can help to identify the source of cognitive and affective obstacles that interfere with mathematics learning. In this study, we analyzed the problem-solving protocols of mathematical gifted children from the perspective of cognitive language and meta-affect to identify the relationship between the functional characteristics of the text and metaphor they use and the functional characteristics of meta-affect. As a result, the behavior of the cognitive and affective characteristics of mathematically gifted children differed according to the success of problem solving. In the case of unsuccessful problem-solving, the use of metaphor as an internal representation system was relatively more frequent than in the successful case. In addition, while the cognitive linguistic aspects of metaphors play an important role in problem-solving, meta-affective attributes are closely related to the external representation of metaphors.

• Journal of Agricultural Extension & Community Development
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• v.29 no.4
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• pp.225-250
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• 2022

This study aims to examine the effects of infant's social development forest experience activities through meta-analysis. The final nine studies(total of 165 in the experimental group and 159 in the control group) were selected as a method of systematic review. Meta-analysis on overall effect size estimation, chi-square test, significance analysis, publication bias analysis, and subgroup analysis was performed using the R program. The overall effect size of 9 studies was 1.59, indicating a large effect size. As a result of subgroup analysis of the sub-factors of sociality, autonomy showed the largest effect size at 1.47, the adjusted effect size of cooperation was 1.34, the effect size adjusted for peer interaction was 1.29, and the adjusted effect size for perspective-taking ability was 0.97. All were found to have a statistically significant effect. To analyze the moderating effect, a meta-regression analysis was conducted on the participation period(4, 5~6, 7~8weeks), the number of sessions(6~10, 11~15, 16~20), the frequency per week(1, 2, 5), and the participation time(40, 60, 90, 120, 150min), but there was no statistical difference. Although not statistically significant, the effect size was larger when the participation period was 4 weeks, the number of sessions was 16 to 20, the frequency was 2 times per week, and the participation time was 40 minutes. This results can be usefully utilized by policy makers and forest commentators related to the vitalization of forest education through forest experience activities.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.