• Title/Summary/Keyword: Repeated learning

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Effects of experience-based learning program using singing insects (소리곤충을 이용한 체험학습프로그램의 학습효과)

  • Kim, So Yun;Kim, Seong Hyun;Jung, Jong Cheol;Lee, Kwang Pum;Kim, Nam Jung
    • Journal of Sericultural and Entomological Science
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    • v.51 no.2
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    • pp.114-118
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    • 2013
  • This study examines how an experimental-based learning program using singing insects improves the academic achievement of elementary school children. Study subjects were a total of 123 elementary students at their 2nd and 3rd grade who voluntarily participated in an experience-based learning program organized by Seodaemun Museum of Natural History, Seoul. Students were given the same survey questionnaires before and after taking the learning program, and this procedure was repeated in 7 independent replication trials. Result from a paired t-test indicated that the learning program had a positive effect on academic performance, with students gaining significantly higher mean scores in the survey test after taking the program than before. This result suggests that the experience-based learning program using singing insects is effective at improving student's academic achievement. Our study provides a critical impetus for developing a variety of other experience-based learning programs using insects like ours, leading us to anticipate that these programs will be practiced more systematically and actively in the classroom in future.

Study on Q-value prediction ahead of tunnel excavation face using recurrent neural network (순환인공신경망을 활용한 터널굴착면 전방 Q값 예측에 관한 연구)

  • Hong, Chang-Ho;Kim, Jin;Ryu, Hee-Hwan;Cho, Gye-Chun
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.22 no.3
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    • pp.239-248
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    • 2020
  • Exact rock classification helps suitable support patterns to be installed. Face mapping is usually conducted to classify the rock mass using RMR (Rock Mass Ration) or Q values. There have been several attempts to predict the grade of rock mass using mechanical data of jumbo drills or probe drills and photographs of excavation surfaces by using deep learning. However, they took long time, or had a limitation that it is impossible to grasp the rock grade in ahead of the tunnel surface. In this study, a method to predict the Q value ahead of excavation surface is developed using recurrent neural network (RNN) technique and it is compared with the Q values from face mapping for verification. Among Q values from over 4,600 tunnel faces, 70% of data was used for learning, and the rests were used for verification. Repeated learnings were performed in different number of learning and number of previous excavation surfaces utilized for learning. The coincidence between the predicted and actual Q values was compared with the root mean square error (RMSE). RMSE value from 600 times repeated learning with 2 prior excavation faces gives a lowest values. The results from this study can vary with the input data sets, the results can help to understand how the past ground conditions affect the future ground conditions and to predict the Q value ahead of the tunnel excavation face.

A Course Scheduling Multi-Agent System For Ubiquitous Web Learning Environment (유비쿼터스 웹 학습 환경을 위한 코스 스케줄링 멀티 에이전트 시스템)

  • Han, Seung-Hyun;Ryu, Dong-Yeop;Seo, Jeong-Man
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.4 s.36
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    • pp.365-373
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    • 2005
  • Ubiquitous learning environment needs various new model of e-learning as web based education system has been proposed. The demand for the customized courseware which is required from the learners is increased. the needs of the efficient and automated education agents in the web-based instruction are recognized. But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the learning weakness which is observed in the continuous feedback of the course. In this paper we propose a multi-agent system for course scheduling of learner-oriented using weakness analysis algorithm via personalized ubiquitous environment factors. First proposed system analyze learner's result of evaluation and calculates learning accomplishment. From this accomplishment the multi-agent schedules the suitable course for the learner. The learner achieves an active and complete learning from the repeated and suitable course.

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The effects of a maternal nursing competency reinforcement program on nursing students' problem-solving ability, emotional intelligence, self-directed learning ability, and maternal nursing performance in Korea: a randomized controlled trial

  • Kim, Sun-Hee;Lee, Bo Gyeong
    • Women's Health Nursing
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    • v.27 no.3
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    • pp.230-242
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    • 2021
  • Purpose: The purpose of this study was to develop a maternal nursing competency reinforcement program for nursing students and assess the program's effectiveness in Korea. Methods: The maternal nursing competency reinforcement program was developed following the ADDIE model. This study employed an explanatory sequential mixed methods design that applied a non-blinded, randomized controlled trial with nursing students (28 experimental, 33 control) followed by open-ended interviews with a subset (n=7). Data were analyzed by both qualitative and quantitative analysis methods. Results: Repeated measures analysis of variance showed that significant differences according to group and time in maternal nursing performance; assessment of and intervention in postpartum uterine involution and vaginal discharge (F=24.04, p<.001), assessment of and intervention in amniotic membrane rupture (F=36.39, p<.001), assessment of and intervention in delivery process through vaginal examination (F=32.42, p<.001), and nursing care of patients undergoing induced labor (F=48.03, p<.001). Group and time improvements were also noted for problem-solving ability (F=9.73, p<.001) and emotional intelligence (F=4.32, p=.016). There were significant differences between groups in self-directed learning ability (F=13.09, p=.001), but not over time. The three main categories derived from content analysis include "learning with a colleague by simulation promotes self-reflection and learning," "improvement in maternal nursing knowledge and performance by learning various countermeasures," and "learning of emotionally supportive care, but being insufficient." Conclusion: The maternal nursing competency reinforcement program can be effectively utilized to improve maternal nursing performance, problem-solving ability, and emotional intelligence for nursing students.

The Effect on Multiplicative thinking and Multiplicative ability by the Instruction of Modeling Problem Situations (문제 장면의 모델화를 통한 수업이 곱셈적 사고력과 곱셈 능력 신장에 미치는 영향)

  • 남승인;서찬숙
    • Education of Primary School Mathematics
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    • v.8 no.1
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    • pp.33-50
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    • 2004
  • This study is intended to investigate the effect on the development of multiplicative thinking and multiplicative ability by teaching repeated addition, rate, comparison, area-array, and combination problems. Two research questions are established: first, is there any difference of multiplicative thinking between the experimental group(the modeling of problem situation learning group) and the control group(the traditional learning group)\ulcorner Second, is there any difference of multiplicative ability between the experimental group and the control group\ulcorner The treatment process for the experimental group is based on modeling problem situations for nine lesson periods. In order to answer the research questions the chi-square analysis was used for the first research question and the t-test was used for the second one. The findings are summarized as follows: there is no significant difference of multiplicative thinking be1ween the experimental and the control group but there is significant difference of multiplicative ability.

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Repeated Overlapping Coalition Game Model for Mobile Crowd Sensing Mechanism

  • Kim, Sungwook
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.7
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    • pp.3413-3430
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    • 2017
  • With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

A Technique of Statistical Message Filtering for Blocking Spam Message (통계적 기법을 이용한 스팸메시지 필터링 기법)

  • Kim, Seongyoon;Cha, Taesoo;Park, Jeawon;Choi, Jaehyun;Lee, Namyong
    • Journal of Information Technology Services
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    • v.13 no.3
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    • pp.299-308
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    • 2014
  • Due to indiscriminately received spam messages on information society, spam messages cause damages not only to person but also to our community. Nowadays a lot of spam filtering techniques, such as blocking characters, are studied actively. Most of these studies are content-based spam filtering technologies through machine learning.. Because of a spam message transmission techniques are being developed, spammers have to send spam messages using term spamming techniques. Spam messages tend to include number of nouns, using repeated words and inserting special characters between words in a sentence. In this paper, considering three features, SPSS statistical program were used in parameterization and we derive the equation. And then, based on this equation we measured the performance of classification of spam messages. The study compared with previous studies FP-rate in terms of further minimizing the cost of product was confirmed to show an excellent performance.

Soft Ground Settlement Estimation Using Neural Network (인공신경망을 이용한 연약지반 침하량 산정)

  • Roh, Jae-Ho;Won, Hyeo-Jea;Oh, Doo-Hwan;Hwang, Sun-Geun
    • Proceedings of the KSR Conference
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    • 2006.11b
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    • pp.1405-1410
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    • 2006
  • Purpose of this research is that offers basic data for optimized design using neural network method to calculate consolidation settlement in study area. In this research, preformed the neural network method that analyzed the settlement characteristics of soft ground nearby study area. Thus, data base established on ground properties and consolidation settlement of neighboring area. In addition, designed the optimum neural network model for prediction of settlement through network learning and consolidation settlement prediction using consolidation settlement DB and ground properties DB. Optimized neural network model decided by repeated learning for various case of hidden layers. In this study, proposed that the optimized consolidation settlement calculation method using neural network and verified which is the optimized consolidation settlement calculation method using neural network.

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Soft-computing Method for Path Learning and Path Secession Judgment using Global Positioning System (위치정보 기반의 경로 학습 및 이탈 판단을 위한 소프트 컴퓨팅 기법)

  • Ra, Hyuk-Ju;Kim, Seong-Joo;Choi, Woo-Kyung;Jeon, Hong-Tae
    • Proceedings of the KIEE Conference
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    • 2004.05a
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    • pp.144-146
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    • 2004
  • It is known that Global Positioning System(GPS) is the most efficient navigation system because it provides precise position information on the all areas of Earth regardless of metrology. Until now, the size of GPS receivers has become smaller and the performance of receivers has become higher. So receivers provide the position information of not only static system but also dynamic system. Usually, users make similar movement trajectory according to their life pattern and it is possible to build up efficient database by collecting only the repeated users' position. Because position information calculated by the receiver is erroneous about 10-30m within 5% error tolerance, the position information is oscillated even on the same area. In this paper, we propose the system that can estimate whether users are out of trajectory or in dangerous situation by soft-computing method.

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Detection of Political Manipulation through Unsupervised Learning

  • Lee, Sihyung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.4
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    • pp.1825-1844
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    • 2019
  • Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.