• Journal of Engineering Education Research
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• v.15 no.6
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• pp.34-42
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• 2012

Problem solving by homework assignment is a process of practicing what were discussed in classrooms and thus is considered as an essential part of learning procedure in engineering education. We introduced the concept of cooperative learning, Student Teams-Achievement Division(STAD), to improve the students' learning efficiency by in-class problem solving. The instructor explained fundamental concepts, and lecture materials were handed out to compensate for the time of in-class team activity. Brief tests were given after every chapter, and team-based additional credits were given for the improvement comparing the average of previous tests of each student. This attempt of modified STAD was evaluated to have brought about a significant improvement in students' academic achievement, in addition to activating classroom atmosphere.

• Journal of Engineering Education Research
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• v.18 no.1
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• pp.61-73
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• 2015

The purpose of this study is to suggest some direction to support learning of students in college of engineering. It results from the assumption that engineering education accreditation should come with assessment of the educational process. To analyze the learning process, this study analyzed 5 categories - involvement in and out of instruction, faculty-student interaction, teaching-learning outcomes, and the system of student support. The Research method was questionnaire, and T-test and hierarchical linear model were used. The major findings are as follows. Major-level of satisfaction in teaching-learning and optional-level of satisfaction in teaching-learning are good. But the degree of self-directed learning activities and student-faculty interaction is low, and writing attitude and learning outcomes are not good. Student-faculty interaction, high-order thinking activities and active involvement have a good influence on learning outcomes. So this study suggests to enhance active involvement in instruction, high-order thinking activities, writing skills, and interaction with faculty for the improvement of quality of higher education.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.422-425
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• 2004

Many believe that electronic distance teaming education transform higher education, saving money and improving learning qualify So, the open University, which teaches around 280,000 students at a distance, is examining the adaption of its distance teaching methods for the internet. But, there are only one type of distance learning education of one way direction. To understand all of a student which selected some of e teaming course, teacher must check that how many student to understand and what is the difficult problems. Without checking this condition, It will be a very difficult and boring distance learning course. In this paper, we introduce of intelligent learning contents of full duplex direction that teach understanding student and not understanding student. The computer simulation results confirms that full duplex e learning system has been proven to be much more efficient than one way direction which not considering about understanding problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.878-879
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• 2021

Most state-of-the-art CNNs for action recognition are based on a two-stream architecture: RGB frames stream represents the appearance and the optical flow stream interprets the motion of action. However, the cost of optical flow computation is very high and then it increases action recognition latency. We introduce a design strategy for action recognition inspired by a two-stream network and teacher-student architecture. There are two sub-networks in our neural networks, the optical flow sub-network as a teacher and the RGB frames sub-network as a student. In the training stage, we distill the feature from the teacher as a baseline to train student sub-network. In the test stage, we only use the student so that the latency reduces without computing optical flow. Our experiments show that its advantages over two-stream architecture in both speed and performance.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.306-315
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• 2023

This study proposed an SCA advice system. It is an ontology-based recommender that provides advice on appropriate computer curricula based on the behavior of high school students. The three computer curricula at Chiang Mai Rajabhat University include computer science (CS), information technology (IT), and web programming and security (WEB). This study aims to design the ontology framework for an SCA advice system. The system considers three core ontologies: student, computer-curriculum, and advice. After analyzing student behaviors, the behavior types of CS, IT, and WEB were determined to be SB-2, SB-1, and SB-5, respectively. All subjects in these three curricula were analyzed and grouped into seven groups. Their curricula were synthesized in terms of basic skills, basic knowledge, and characteristics. Finally, advice results can be obtained by consolidating the curriculum nature of the CS, IT, and WEB curricula.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.40-44
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• 2011

In the engineering education, the difference of student's achievements is one of the problems to make teaching more difficult. To solve this problem, cooperative learning can be adopted to make students study automatically and cooperatively in the small study group. However, it is not easy to achieve substantial cooperation in the study group. In this paper, we propose a method to achieve substantial cooperative learning in the engineering subject requiring mathematical practice. we can vitalize the cooperative learning and improve the student's achievements by using the both of individual and group assessment.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.3-7
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• 2012

In 1996, the Faculty of Engineering, Niigata University, Japan entered an era of open student-exchange with Otto-von-Guericke-University Magdeburg, Germany. Thus far, more than 50 of our students have devoted anywhere from three months, to an entire year of their courses, to collaborative efforts with fellow students, (-and some cases, the local citizenry) -in their native environment experiencing unfamiliar education systems and cultures.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.57-67
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilized Holland career search test results, TOEIC score, course work list and GPA score as the input for data mining, and we were able to generate knowledge and rules with regard to the college road map advisory service. Factor analysis and AHP(Analytic Hierarchy Process) were the primary techniques deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained from the human student advice experts.

• Journal of Engineering Education Research
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• v.21 no.6
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• pp.108-117
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• 2018

Given the substantial industrial and economic contributions of university start-ups, a growing number of studies have adopted the ecosystem perspectives to systematically explain creating start-ups in universities. Despite the huge theoretical potential, few studies have analyzed the complex, complimentary interactions of the core components in the university entrepreneurship ecosystems (UEEs). Addressing the limitation, this research not only discusses the role of the core ecosystem components, such as patents, entrepreneurship education, and student entrepreneurship clubs, but also analyzes their discrete and complimentary effects on the productivity of UEEs. Based on a national survey of universities, this study shows that all the core components have a positive effect on the ecosystem productivity. More importantly, this study investigated the complimentary relationships among components and tested the moderation effects of both the entrepreneurship education and the student clubs on the relationship between the patents and the productivity of UEEs. The analysis results show that the student clubs intensify the patents' positive effect on the productivity of UEEs. The research results could provide the crucial policy insights for the successful design of UEEs.

• ETRI Journal
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• v.41 no.2
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• pp.242-253
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• 2019

We devise a layer-wise hint training method to improve the existing hint-based knowledge distillation (KD) training approach, which is employed for knowledge transfer in a teacher-student framework using a residual network (ResNet). To achieve this objective, the proposed method first iteratively trains the student ResNet and incrementally employs hint-based information extracted from the pretrained teacher ResNet containing several hint and guided layers. Next, typical softening factor-based KD training is performed using the previously estimated hint-based information. We compare the recognition accuracy of the proposed approach with that of KD training without hints, hint-based KD training, and ResNet-based layer-wise pretraining using reliable datasets, including CIFAR-10, CIFAR-100, and MNIST. When using the selected multiple hint-based information items and their layer-wise transfer in the proposed method, the trained student ResNet more accurately reflects the pretrained teacher ResNet's rich information than the baseline training methods, for all the benchmark datasets we consider in this study.