• 한국학교수학회논문집
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• 제24권1호
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• pp.59-81
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• 2021

이 연구에서는 학습자가 수학 텍스트를 반복하여 읽을 때 나타나는 수학 학습 과정이 어떻게 변하는지를 조사하였다. 또한 수학 학습 방법으로써 반복 읽기의 효과를 점검하고 보다 효율적인 반복 읽기 교수·학습 전략에 대한 시사점을 모색하였다. 반복 읽기 수학 학습에는 국립대학교 수학교육과에 재학 중인 예비 수학교사 8명이 참가하였다. 예비 수학교사는 각각 4개의 그룹으로 구성되어 그룹에 따라 서로 다른 4개의 주기로 총 3회 반복 읽기를 시행하였다. 수학 학습 자료 읽기 과정에서 나타나는 예비 수학교사의 시선의 움직임을 추적하고 심박수를 측정하였다. 수집한 자료를 회차별 총 읽기 시간, 슬라이드별 총 읽기 시간, 각 회차와 슬라이드별 총 읽기 시간의 변화 추세, 슬라이드 읽기 순서, 회차별 심박수 변화 추세 등의 다섯 가지 측면에서 분석하였다. 첫 번째 읽기에서는 참가자의 대부분이 비슷한 양상을 보였으나, 두 번째와 세 번째 읽기에서는 개별 학습자에 따른 읽기 패턴의 변화가 보다 다양하게 드러났다. 또한, 첫 번째 읽기에서 반복 주기와 무관하게 가장 많은 시간이 소요되었고, 이후 반복적 읽기 시간에서는 개인별로 차이가 나타났다. 연구 결과에서 도출한 가장 중요한 결론은 반복 읽기를 통한 자기 주도적 수학 학습은 주기와 관계없이 효과적이라는 것이다. 추가적으로 반복 읽기 교수·학습 전략의 효율성을 증진시키기 위한 네 가지 전략을 제안하였다.

• 정보교육학회논문지
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• 제18권4호
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• pp.559-566
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• 2014

본 연구는 인지적 발달단계에 대한 신경학적 역동 발달주기를 규명하기 위하여 추상적 발달단계인 추상적 맵핑, 추상적 체계, 단일원리의 각 학습단계별 뇌파의 변화와 역동적 학습발달 간의 관계를 규명하였다. 컴퓨터 수학학습에서 일어나는 자발적 학습은 수학과제를 수행할 때 적은 학습지원 으로 나타나는 학습효과에 중점을 두었으며 이해적 학습은 적절한 학습지원을 통해 나타내는 학습효과를 중심으로 인지적 변화와 뇌파와의 관계성을 통해 뇌와 뇌신경의 발달관점에서 파악한 것이다. 연구 결과, 추상적 맵핑과 추상적 시스템 단계에서 지원을 통한 이해적 학습이 두정엽과 전두엽에서 의미 있는 뇌 활동성을 가져왔으며 추상적 개념학습의 마지막 단계인 단일원리에서는 피험자의 발달단계가 적정나이보다 작아 오히려 지원을 통한 이해적 학습이 더 적은 뇌 활동성을 가져왔다.

• 성인간호학회지
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• 제19권4호
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• pp.624-633
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• 2007

Purpose: This study was conducted to develop an integrated undergraduate course including a PBL based on a blended learning strategy, and evaluate learners' responses. Methods: The learning contents of cardiovascular, respiratory, and musculoskeletal medical systems, and nursing diagnoses of 'activity and rest' domain (NANADA's classification II, 2005) were analyzed. Six clinical scenarios with the clients in different life cycles were developed for PBL. Classical lecture and group presentation with on-line self learning were implemented in addition to PBL. The developed course was implemented on 84 junior nursing students in a university for 7 weeks with 5 hours per day, two days per week. Students were asked to complete structured questionnaires including problem solving, critical thinking, and nursing diagnosis differentiation abilities. Results: Learner's evaluation was positive in problem solving skills and in the differentiation ability of nursing diagnoses relevant to an 'activity and rest' functional health pattern. Conclusion: Development and implementation of integrated courses based on a blended learning method need to be continued to enhance students' thinking and self-directed learning abilities. Supporting strategies for individual learners should be added for successful blended learning such as individual on-line feedback and consideration of individual learning outcomes.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• 전력전자학회논문지
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• 제27권4호
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

• Educational Technology International
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• 제14권1호
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• pp.27-40
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• 2013

Design-Based Research (DBR) focuses on developing key principles of interventions to advance both theory and practicalities of dissemination (Brown, 1992), yet its methodological details have not been quite established. Thus, the purpose of this paper is to address the pragmatics of DBR by sharing the researcher's reflections on conducting a longitudinal DBR project for five years. In an attempt to advance college teaching practices as well as theories related to student plagiarism, the project focused on refining "humble" theories on how and why college students engage in plagiarism to design classroom interventions for promoting academic integrity. Similar to the Integrative Learning Design (ILD) framework proposed by Bannan-Ritland (2003), but conducted in a much simpler and less formal format, this study followed DBR cycles from initial conceptualization to design and enact instructional interventions in authentic contexts while collecting both quantitative and qualitative data from each phase. Finally, the paper addresses some challenges encountered throughout the DBR project as well as the lessons learned from this experience. Like many previous DBR studies whose practical relevance is limited to local context, the findings from this study may not be easily generalized for other contexts.

• 한국초등과학교육학회지:초등과학교육
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• 제35권2호
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• pp.166-180
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• 2016

This paper examines the effects on academic achievement, scientific process skills and affective domain for elementary students learning the 'Chicken life cycle' through traditional science class versus a smart media based STEAM approach. Students designed and built a hatching jar and created a smart media content for chickens using time-lapse technology. This STEAM program was developed to improve their scientific concepts of animals over nine periods of classes using integrated education methods. The experimental study took place in the third grade of public schools in a province, with the STEAM approach applied in 2 classes (44 students) and the traditional discipline approach implemented in 2 classes (46 students). The STEAM education significantly influenced the improvement of academic achievements, basic scientific process skills and affective domain. The results suggest that this STEAM approach for teaching scientific concepts of animal life cycles has the performance in terms of knowledge, skills and affect gain achievements in elementary school students' learning when compared to a traditional approach. Moreover, the smart media based STEAM program is helpful to lead students to engage in integrated problem-solving designs and learning science and technology.

• 산업경영시스템학회지
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• 제43권1호
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• 품질경영학회지
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• 제51권3호
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• 한국정밀공학회지
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• 제22권3호
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• pp.170-178
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• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.