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Development of an Web-Based English Learning System for Middle Schools (웹에 기반한 중학교 영어학습시스템의 개발)

  • Kim, Heung-Hwan;Woo, Je-Seok
    • The Journal of Korean Association of Computer Education
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    • v.8 no.2
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    • pp.41-51
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    • 2005
  • Although distance education system based on WBI theory has already been generalized in domestic universities and some private academical institutes, the use of the system in the real fields of schools is in the early stage now. In this paper, we develop a model of English learning system for middle school students and improve students' learning methods through the system. The system also makes students study all the learning topics which they choose in the system freely and repeatedly. It was applied to the middle school students. The analysis of the application showed the following results. First, it was very effective for students to achieve the learning objectives of the course of English. Second, the system made students improve their abilities to study English and also increase their abilities to surf the Internet and glean useful information. Third, the system made it possible for students to accomplish individual learning.

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A Study on Development of Integrating Mathematics and Coding Teaching & Learning Materials Using Python for Prime Factorization in 7th Grade (파이썬을 활용한 중학교 1학년 소인수분해의 수학과 코딩 융합 교수·학습 자료 개발 연구)

  • Kim, Ye Mi;Ko, Ho Kyoung;Huh, Nan
    • Communications of Mathematical Education
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    • v.34 no.4
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    • pp.563-585
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    • 2020
  • This study developed teaching-learning materials for mathematics and coding convergence classes using Python, focusing on 'Prime Factorization' of seventh graders. After applying the teaching methods and contents to the students, they analyzed whether the learners achieved their learning goals. The results were used to modify and supplement teaching and learning materials. Affective domain of learners were also analyzed. The results are that the teaching methods and contents of the developed teaching-learning materials were generally appropriate for learners. The learners understood most of the lessons according to the set teaching methods of all classes. And learners have mostly reached their learning goals. In addition, as a result of analyzing the definition characteristics of learners through follow-up interviews, the interest in mathematics and programming has improved. The developed teaching and learning materials of this study are well consisted mostly of the teaching methods and the contents of the classes, and are organized so that learners can reach most of the learning goals. It also brought positive changes to the affective domain of mathematics and coding, demonstrating the potential for useful use in school.

Recent Trends in the Application of Extreme Learning Machines for Online Time Series Data (온라인 시계열 자료를 위한 익스트림 러닝머신 적용의 최근 동향)

  • YeoChang Yoon
    • The Journal of Bigdata
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    • v.8 no.2
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    • pp.15-25
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    • 2023
  • Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.

A Study on the Effect of STAD Group Study using Gradual Self-Leading Learning Materials on the Accomplishments of Math Curriculum (자기주도적 수준별 학습지를 이용한 STAD 협동학습이 수학교과 학습 성취도에 미치는 효과)

  • 송영무;나덕수
    • Journal of the Korean School Mathematics Society
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    • v.6 no.1
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    • pp.65-85
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    • 2003
  • The purpose of this research is to increase mathematical problem solving abilities VIa STAD evaluation after completing classes. to which ST AD group study is applied, and promoting the learning accomplishments of students by developing gradual self-leading learning materials about the research project on ' How to use an hour math class efficiently\ulcorner ' For this purpose, the items below were studied. Firstly, gradual self-leading learning materials were developed and applied which were composed of textbook abstracts, basic problems, developing problems and intensive problems rather than existing textbooks. Secondly, the ST AD group study model was selected and applied which invokes competitions among small groups of which learning goals were clear. individual responsibility was important. and successive opportunities were equal. The evaluation using STAD at each end of a chapter was announced instantly using the EXCEL scoring system. Though the results of experimental classes were limited in their size. experimental time, and class selection, there were meaningful changes in the aspect of being able to heighten the accomplishment desire of students by inducing voluntary competitions among small groups without any student omitted. As the result of applying this research to my class, the ST AD group study using gradual self-leading learning materials invoked the interests of students and increased learning accomplishments via increasing problem solving abilities in mathematics. The ST AD group study was easy to use by beginning teachers, and its process was simple. It increased interactions among students and learning motives because its compensation system was open to all students. Among various studying methods for small groups. STAD group study is expected to be widely used for mathematics classes.

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Development and Evaluation of a Web-based Education Program on Appropriate Antibiotic Use in Korean Adolescents (청소년의 올바른 항생제 사용을 위한 웹 기반 교육프로그램 개발 및 평가)

  • Kim, So-Sun;Cheon, Joo-Young;Kwon, In-Sook;Cho, Yoon-Mi;Moon, Seong-Mi
    • Journal of Korean Academy of Fundamentals of Nursing
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    • v.18 no.3
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    • pp.383-391
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    • 2011
  • Purpose: This study was done to develop a web-based education program on appropriate antibiotic use and test the effects of the program on knowledge and attitudes towards antibiotic use in Korean adolescents. Methods: The web-based education program was developed through an extensive literature review and professional advisory meetings including technical assistance from a web-based education programmer and content experts. A convenience sample of 851 students from middle and high schools participated in the assessment of effects of the program. Knowledge and attitudes of the students towards antibiotic use and satisfaction with the web-based education program were measured. Descriptive statistics and paired t-test were used to analyze the data. Results: There were significant improvements in knowledge and attitudes towards antibiotic use following self-learning via the web-based education program in both middle and high school students. High school students demonstrated higher scores in knowledge and attitudes than middle school students. Conclusion: The results of this study indicate that this web-based education program on appropriate antibiotic use is a convenient and effective medium for self-learning in adolescents. Therefore the web-based program should be put into wide use as an effective and convenient teaching method for health education in secondary schools.

Learning Process Support Experience of Cerebral Palsy Children's mothers (뇌성마비 아동 어머니의 학업과정 지지 경험)

  • Baek, Kyoung-Seon
    • Journal of East-West Nursing Research
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    • v.7 no.1
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    • pp.48-60
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    • 2002
  • This study was conducted to understand and analyze experience of learning process support toward mothers of children who suffer from Cerebral Palsy, to generalize and structurize the meaning of practical learning process support, and to use the study results as basic materials for development of support model. Study subjects were 12 mothers who have Cerebral Palsy children attending an ordinary school and a school for handicapped children. Data were collected from November 10, 1999 to December 29, 2000 and from January 20 to March, 2001, for 2 months. Data were collected from un-structural and open questions. And the collected data were analyzed with the phenomenological analysis method proposed by van Kaam(1969). Study results obtained from this report were as follows; As for original materials about learning process support experience of cerebral palsy children's mothers, 48 technical expressions were derived from 97 pieces of original materials, they were categorized into 10 common elements. Those common elements were , , , , , , , , , . Based on the above results, it is suggested that the concept of learning process support toward children suffering from Cerebral Palsy should be structureized, and proper models should be developed.

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Training Data Sets Construction from Large Data Set for PCB Character Recognition

  • NDAYISHIMIYE, Fabrice;Gang, Sumyung;Lee, Joon Jae
    • Journal of Multimedia Information System
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    • v.6 no.4
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    • pp.225-234
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    • 2019
  • Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

The design method for a vector codebook using a variable weight and employing an improved splitting method (개선된 미세분할 방법과 가변적인 가중치를 사용한 벡터 부호책 설계 방법)

  • Cho, Che-Hwang
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.39 no.4
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    • pp.462-469
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    • 2002
  • While the conventional K-means algorithms use a fixed weight to design a vector codebook for all learning iterations, the proposed method employs a variable weight for learning iterations. The weight value of two or more beyond a convergent region is applied to obtain new codevectors at the initial learning iteration. The number of learning iteration applying a variable weight must be decreased for higher weight value at the initial learning iteration to design a better codebook. To enhance the splitting method that is used to generate an initial codebook, we propose a new method, which reduces the error between a representative vector and the member of training vectors. The method is that the representative vector with maximum squared error is rejected, but the vector with minimum error is splitting, and then we can obtain the better initial codevectors.

Two Stage Deep Learning Based Stacked Ensemble Model for Web Application Security

  • Sevri, Mehmet;Karacan, Hacer
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.2
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    • pp.632-657
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    • 2022
  • Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

Impacts of Badges and Leaderboards on Academic Performance: A Meta-Analysis

  • KIM, Areum;LEE, Soo-Young
    • Educational Technology International
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    • v.23 no.2
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    • pp.207-237
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    • 2022
  • As technological changes continue to accelerate every day, meeting the needs of a shifting educational landscape requires leaving an exclusively "in-person" education behind. Gamified learning environments should be carefully designed in light of conflicting studies to suit students' needs. The purpose of this meta-analysis is to draw conclusive results regarding the application of the most commonly used game elements in education, i.e., badges and leaderboards, through a comprehensive analysis of their impact on academic performance in online learning. Review Manager (RevMan 5.4) was used to analyze eligible studies selected from Emerald, SAGE, ERIC, EBSCO, and ProQuest between January 2011 and January 2022. Analyzing 37 studies found that using leaderboards and badges in online education enhanced academic performance when compared to traditional learning without gamification (SMD = 0.39). The badge-only intervention showed a larger effect size (SMD = 0.33) than the leaderboard-only intervention (SMD = 0.27). Badges and leaderboards together exhibited a larger effect size (SMD = 0.48) than individual game elements (SMD = 0.40). The impact of the game elements on academic performance was greater in the humanities (SMD = 0.51) than in STEM fields (SMD = 0.32) and was greater for K-12 students (SMD = 0.63) than for college students (SMD = 0.31). This study contributes to a timely discussion of the use of badges and leaderboards in COVID-19 online learning trends and provides relevant data for designing integrations of online education and gamification models.