• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.299-311
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• 2023

This study is to verify the development process and effectiveness of a learning strategy program for junior college students. The necessity and feasibility of learning strategy programs for junior college students were confirmed and reviewed through prior studies, and the program development process was divided into four stages: program planning, program development, program execution, and program evaluation, and integrated into program development management. In the first stage of program planning, the development direction, goals and objectives were set. In the second stage of program development, prior research analysis, content selection, program organization, and evaluation plan were conducted. In the third stage of execution, the program was executed, and in the fourth stage of evaluation, program evaluation was conducted to develop the program. In order to verify the effectiveness of the program, the test was conducted using the learning strategy diagnostic scale for junior college students to collect data, and the effectiveness was verified for the pre-, post-, and post-test scores.

• The Mathematical Education
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• v.52 no.4
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• pp.443-464
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• 2013

The purpose of this study is to identify the first graders' mathematics learning abilities in elementary school. To do this, I examined the first graders on their mathematics learning abilities, and analyzed the test results. Although all of the evaluation factors are composed the mathematical contents which are taught in elementary school, the correct answer rate of them were very high. The results of this study will be used as the basis. for developing the mathematics curriculum and textbook in elementary school. Also first grade teachers can use the results of this study as a basis when they plan a instruction for the first graders.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.2
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• pp.236-248
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• 2008

This study has explored the elements of e-teaching portfolio for enhancing teaching expertise in higher education. This study is carried out through the literature review and expert's focus group interview. As the result of this study, seven elements of e-teaching portfolio for enhancing teaching expertise in higher education have been found. First, 'personal background' include curriculum vitae, course responsibility, and other educational activities. Second, 'teaching philosophy' include the principals on teaching and learning, statements of teaching philosophy. Third, 'learning environment' include the characteristics of students, the previous learning contents, and physical environment. Forth, 'course contents and methods' include teaching strategies and instructional materials, Fifth, 'instructional evaluation' includes the principals of evaluation and the examples of learning outcomes. Sixth, 'endeavor for improvement of instruction' include evidence of activity for teaching improvement and instruction feedback from peer and students. And e-teaching portfolio also includes research career and awards history element.

• Journal of Engineering Education Research
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• v.20 no.3
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• pp.42-49
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• 2017

This study is about the influence of recording reflective journals and receiving feedback from professors on academic achievements in technical writing. We analyzed the differences between the test group who had recorded reflective journals and getting feedback, and the control group who had gotten feedback without reflective journals. And we compared academic achievements by conducting both professor evaluation and peer evaluation in technical writing. The results showed better learning effect, learning satisfaction and academic achievements in the test group than the other.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.346-347
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• 2017

본 논문에서는 컴퓨터 프로그래밍 수업에서 정규 수업과 연계하여 학습자들의 수준별 맞춤형 학습 활동을 별도로 진행할 경우, 학습자들의 학습 수준을 평가하여 수준별 팀 구성을 하게 되는데 학습 수준을 평가하기 위해 프로그래밍 작성 능력과 같은 학습실력 이외에 학습 의욕이나 도전 정신 등 다른 요소들을 함께 반영하여 팀 구성을 하게 되면 어떤 학습 효과를 거둘 수 있는지에 대해 연구해보고자 한다.

• The Mathematical Education
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• v.54 no.1
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• pp.31-47
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• 2015

School Math Field Trips(SMFT) for School Mathematics can be defined as teaching and learning activity of mathematics going into the field of Korean history, culture, science and technology. This is a literature analysis study to systemize teaching and learning method of mathematics based on literature analysis and real SMFT activity. First, SMFT was introduced to improve cognitive affective and cultural-mathematical teaching and learning method of mathematics. Second, SMFT has three purposes of cognitive, affective and cultural-mathematical. Third, to conduct mathematical education activity the direction of teaching was set. Forth, the progressing way of developing material and SMFT was researched. Fifth, developing the evaluation standard of SMFT and evaluation method was suggested.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.53-64
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• 2021

The purpose of this study is to provide implications for designing and implementing non-face-to-face online classes at the College of Engineering in the post-corona era by analyzing the instructors' evaluations and experiences of non-face-to-face online classes operated in the COVID-19 pandemic. According to the overall evaluation results of non-face-to-face online classes from instructors at the College of Engineering, 'instructional design' was the highest among the five areas including instructional design, learning management, learning support, learning evaluation, and instructional outcomes. In addition, the effectiveness of non-face-to-face online experimental or practical classes was found to be relatively low. The results of this study imply that the instructors need to consider several instructional strategies such as active interaction with learners, clear explanation, and the use of technology in non-face-to-face online engineering classes.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.