• Research in Mathematical Education
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• v.20 no.1
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• pp.21-29
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• 2016

Metacognition means "thinking about one's own thinking". There are generally two aspects of metacognition: i) Reflection - thinking about what we know; and ii) Self-regulation - managing how we go about learning. Developing metacognitive abilities is not simply about becoming reflective learners, but about acquiring specific learning strategies as well. There are several strategies that may be used by teachers to develop metacognitive skills amongst learners. As part of a Professional Development project secondary school mathematics teachers have been developing their knowledge and skills to teach for metacognition. In this paper we analyze two lessons presented by groups of teachers in the project and tease out similarities and differences between the lessons that afford or hinder the development of metacognitive skills of learners.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.1
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• pp.289-299
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• 2016

The purpose of the study was to develop evaluation methods which would measure student achievement and progress without traditional paper-pencil tests such as mid-term and final examinations. More specifically, the main research focus were to establish general directions of student assessment during free semester, to build evaluation models supporting student's participation and learning, and to report and record various student evaluation results. As research results, we found that student evaluation of free semester should be organized to improve a) experience learning activity, self-regulatory and collaboratory study, b) high-order thinking ability and character-building, and c) teacher-student-parent cooperation. Since traditional paper-pencil tests were restricted in free semester, student achievement should be provided by a way of performance descriptions on transcripts rather than quantitative grade points. Student performance descriptions had to show not only subject knowledge but also students efforts, motivation, and participation. These multiple and educationally meaningful information would be collected by teacher-student-parent communication and collaboration.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.66-79
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• 2018

The purpose of this study is to explore how pre-service elementary teachers perceive and use smart learning environments. For this purpose, 23 pre-service elementary teachers who took theory and practice in a science education course were asked to develop lesson materials using smart tools and make a self-report questionnaire. These data were categorized in an instructional, exploratory, and interactive approach, depending on how they guided students to access knowledge and information. As a result of the study, pre-service teachers perceived the smart tools as the exploratory and interactive learning tools to be used for students to actively search for and interact with data and knowledge. But in developing lesson materials, they usually used the smart tools for resource sharing and communication in the instructional manner. In conclusion, the gap between their perception of smart tools and lesson materials, and the educational implications will be discussed.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.31-35
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• 2005

In this paper, we designed and implemented the web-based instruction system to improve quality of education and achieve efficient instruction-learning throughout the WBI. This web-based evaluation system lets learners make up their lesson in a self-oriented and effective way, by letting instructors diagnose learners level of understanding trigonometric function and letting learners take part in evaluation as well. The system also lets instructors easily make out items for evaluation by using 한글(Korean Language Word processor) and present them on the Web.

• Asia pacific journal of information systems
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• v.5 no.1
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• pp.36-51
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• 1995

본 논문에서는 비구조적인 의사결정문제를 효과적으로 해결하기 위하여 감독학습 인공신경망 모형과 비감독학습 인공신경망 모형을 결합한 하이브리드 인공신경망 모형인 HYNEN(HYbrid NEural Network) 모형을 제안한다. HYNEN모형은 주어진 자료를 클러스터화 하는 CNN(Clustering Neural Network)과 최종적인 출력을 제공하는 ONN(Output Neural Network)의 2단계로 구성되어 있다. 먼저 CNN에서는 주어진 자료로부터 적정한 퍼지규칙을 찾기 위하여 클러스터를 구성한다. 그리고 이러한 클러스터를 지식베이스로하여 ONN에서 최종적인 의사결정을 한다. CNN에서는 SOFM(Self Organizing Feature Map)과 LVQ(Learning Vector Quantization)를 클러스터를 만든 후 역전파학습 인공신경망 모형으로 이를 학습한다. ONN에서는 역전파학습 인공신경망 모형을 이용하여 각 클러스터의 내용을 학습한다. 제안된 HYNEN 모형을 우리나라 기업의 도산자료에 적용하여 그 결과를 다변량 판별분석법(MDA:Multivariate Discriminant Analysis)과 ACLS(Analog Concept Learning System) 퍼지 ARTMAP 그리고 기존의 역전파학습 인공신경망에 의한 실험결과와 비교하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.2
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• pp.276-285
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• 2014

The purpose of this study is to investigate the pattern and characteristics of elementary school teaching and learning processes in science based classes. The study participants' class was recorded in video and instructional conversation transcription. The pattern of the observed class was analyzed using the classification frame suggested by Mogan &Saxton(2006). In result, the questioning for elicit information was most frequent and questioning for shape understanding and the questioning for press for reflection in its priority. In result, the presence of elicited questioning for the attainment of knowledge and understanding is more prominent in science-based classrooms. It was revealed that the participating teachers used the questioning sentence pattern more frequently and the self-sustained inquiry that accelerates creative thinking of the student was lacking. It was discovered that teaching elicited questioning, which accelerates creative thinking, as well as fact confirmation pattern is a necessary element of training for teachers.

• School Mathematics
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• v.1 no.2
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• pp.433-449
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• 1999

This paper introduces a branch of qualitative method, called an in-depth interview method. By collecting data from the stories of Korean middle school students and a 9th grade American girl who used Geometer's Sketchpad and various software respectively for their mathematical problem solving, the qualitative analysis leads us to understand how such software affect their lives with mathematics subject. The unique characteristics and strands of each student's utterances reflect how software plays a role of learning aid for their mathematics learning. The arm of this study is both to get a good picture of each student's self-perceived relationship to mathematics as well as to explore external and objective parameters and factors in each student's internal situations. The qualitative descriptions of the collected data help us guide the students to the points where they could develop their interests and satisfaction with subject matter better. In this way, teachers may have more realistic understandings of how students become interested and motivated by mathematics, so that they are better able to find out ways of grasping the totalities of how the use of technology is interwoven into the school curricular.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.17-18
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• 2017

본 논문은 스마트 팩토리의 중요 요소인 무인반송차(AGV)를 자율 주행시키기 위해 오픈 소스 자율 주행차 시뮬레이터인 udacity를 이용해 머신 러닝시키는 시스템을 개발하였다. 공장의 운행 루트를 자율주행 시뮬레이터의 전경으로 가공하고, 3개의 카메라를 부착시킨 AGV를 운행시키면서 머신 러닝시킨다. AGV를 주행하여 얻어진 여러 학습 데이터를 통해 도출된 결과들을 각각 비교하여 우수한 모델을 선정하고 운행시킨 결과 AGV가 정해진 운행 루트를 정확하게 주행하는 것을 확인하였다. 이를 통해, 가상 운행 환경에서 저비용으로 AGV 운행 학습이 가능하다는 것을 보였다.