• Title/Summary/Keyword: Approaches to Learning

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Proposing Evaluation Procedures for Blended Instruction

  • OH, Eunjoo
    • Educational Technology International
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    • 제12권2호
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    • pp.47-70
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    • 2011
  • The purpose of this paper was to develop evaluation procedures for blended instruction, focusing on the courses that are currently offered in the university. This study analyzed current evaluation procedures and instruments and suggested redesign the evaluation process for blended instruction. The evaluation procedures are designed based on the combination of objective-oriented and consumer-oriented evaluation approaches. It includes three stages: front-end (screening), formative evaluation, and summative evaluation. During the front-end evaluation stage, information regarding students' technology skills and attitudes towards online instruction and classroom instruction are suggested to collect and plan the instructional strategies accordingly. The formative evaluation is conducted during the semester to collect students' opinions about the course and instructors modify their instruction based on the evaluation results. At the end of semester, summative evaluation is to be conducted to collect the data to improve the course. Evaluation questions and components for each stage are developed to collect the data such as students' perceptions of the course, the usefulness of online instructional materials, the effectiveness of blended learning strategies, and students' satisfaction with the course.

Structural Crack Detection Using Deep Learning: An In-depth Review

  • Safran Khan;Abdullah Jan;Suyoung Seo
    • 대한원격탐사학회지
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    • 제39권4호
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    • pp.371-393
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    • 2023
  • Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

Improving Malicious Web Code Classification with Sequence by Machine Learning

  • Paik, Incheon
    • IEIE Transactions on Smart Processing and Computing
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    • 제3권5호
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    • pp.319-324
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    • 2014
  • Web applications make life more convenient. Many web applications have several kinds of user input (e.g. personal information, a user's comment of commercial goods, etc.) for the activities. On the other hand, there are a range of vulnerabilities in the input functions of Web applications. Malicious actions can be attempted using the free accessibility of many web applications. Attacks by the exploitation of these input vulnerabilities can be achieved by injecting malicious web code; it enables one to perform a variety of illegal actions, such as SQL Injection Attacks (SQLIAs) and Cross Site Scripting (XSS). These actions come down to theft, replacing personal information, or phishing. The existing solutions use a parser for the code, are limited to fixed and very small patterns, and are difficult to adapt to variations. A machine learning method can give leverage to cover a far broader range of malicious web code and is easy to adapt to variations and changes. Therefore, this paper suggests the adaptable classification of malicious web code by machine learning approaches for detecting the exploitation user inputs. The approach usually identifies the "looks-like malicious" code for real malicious code. More detailed classification using sequence information is also introduced. The precision for the "looks-like malicious code" is 99% and for the precise classification with sequence is 90%.

A study on the optimal tracking problems with predefined data by using iterative learning control

  • Le, Dang-Khanh;Le, Dang-Phuong;Nam, Taek-Kun
    • Journal of Advanced Marine Engineering and Technology
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    • 제38권10호
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    • pp.1303-1309
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    • 2014
  • In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

IC-PBL 기반의 패션 소비트렌드 분석 수업 개선 및 교육적 효과 (Improvement and Educational Effectiveness of Fashion Consumption Trend Analysis Class Based on IC-PBL)

  • 이재경
    • 패션비즈니스
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    • 제27권5호
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    • pp.121-134
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    • 2023
  • With the development of information and communication technology, interest in new educational approaches that can enhance the learning performance of learners with improved information literacy skills is increasing, and universities are actively promoting educational innovation to foster the talents required by society. In the field of fashion studies education, which is closely related to the fashion industry, there is a strong need to develop field-linked educational programs that reflect the trends in the industry and changes in the educational system. The purpose of this study was to introduce industry-coupled problem-based learning (IC-PBL) to the course "Understanding Fashion Consumption Trends" for non-fashion majors to reflect the current needs and strengthen the educational effectiveness of the learners through a survey. A seven-step curriculum (introduction to the class, practitioner's problem, learner's problem analysis, organizing concepts related to variables, information collection and scenario writing, presentation and scenario proposal, and evaluation) not only enhanced learners' understanding of fashion consumption trends and the fashion industry but also greatly amplified learners' satisfaction with the class. The results of the survey showed that the seven-step curriculum was effective in increasing learners' self-directed learning ability, problem-solving ability, and confidence in learning. Self-directed learning ability was stronger than other factors, consistent with the core principle of problem-based learning to empower learners to take the initiative and promote self-directed learning. Each factor analyzed was positively correlated.

Text Classification Using Parallel Word-level and Character-level Embeddings in Convolutional Neural Networks

  • Geonu Kim;Jungyeon Jang;Juwon Lee;Kitae Kim;Woonyoung Yeo;Jong Woo Kim
    • Asia pacific journal of information systems
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    • 제29권4호
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    • pp.771-788
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    • 2019
  • Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naïve Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character-level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.

Integrative Cognitive-Affective Learning in a Primary Science Lesson

  • Siang, Tan Kok;Santhanasamy, S. Nirmala Devi
    • 한국과학교육학회지
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    • 제32권6호
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    • pp.1039-1049
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    • 2012
  • The first category of Affective Domain objectives in Bloom's Taxonomy is about "Receiving". In it, the first subdivision listed is "Awareness" (Krathwohl, Bloom & Masia, 1964). Since these categories are intended to be hierarchical in ascending order of internalization, it is important that young learners be given ample opportunities in their learning experiences in class to be aware of positive values and effective life skills. This paper reports a feasibility study on the adoption of an integrative cognitive-affective learning approach in a primary school science lesson. 37 primary six students in a Singapore primary school were taught the concept of centre of gravity, including a hands-on activity to find the centre of gravity of an irregularly shaped cardboard by using a plumbline. After reviewing how a plumbline works, their teacher then led them into a discussion on the question "Who is the plumbline in your life?" a reference to identifying positive role models in their lives. From the transcript of the students' in-class sharing and their written responses to the question, it is clear that the integrative cognitive-affective learning approach did enable students to present their ideas and learning experiences in the affective domain quite readily. This conclusion provides a valuable lead to a follow-up project on whether students who are exposed to such integrative learning approaches will be more capable and more aware of identifying important positive social habits or values. If so, then the teaching of values in schools could take on a whole new dimension, that of borrowing students' learning energy in the cognitive domain to learn values and life skills in the affective domain.

'모두를 위한 과학교육'을 실현하기 위한 과학 학습 정체성에 대한 사회문화적 접근 연구 동향 분석 (Research trend on the sociocultural approaches to science learning identity for the realization of 'Science Education for All')

  • 황세영
    • 한국과학교육학회지
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    • 제38권2호
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    • pp.187-202
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    • 2018
  • 본 연구에서는 과학교육에서 다양한 학습자를 포용한다는 것은 과학 학습 참여와 소외 현상에 작동하는 사회문화적 기제를 분석하는 것이 전제되어야 한다는 문제의식 하에 관련 연구 동향을 고찰하고자 하였다. 이를 위해 과학 학습 정체성에 대한 사회문화적 접근으로 수행된 해외 학술지 논문 85편을 분석하였다. 논문 분석 결과는 먼저 연도별, 국가별 출판 수와 같은 기본적인 현황과 함께 연구 대상으로 삼은 학습자의 사회문화적 배경, 연구 맥락(상황), 연구 방법에 대한 범주별 현황을 제시하였다. 다음으로는 주요 연구 문제와 주제에 대한 이론적 틀과 구체적인 연구 사례를 제시함으로써 해당 연구 주제에 대한 보다 심층적인 분석을 시도하였다. 분석 결과 해당 분야 연구는 과학 학습을 공동체에서 참여함으로써 정체성을 발달해가는 과정으로 바라본다는 점에서 학습자가 지닌 다양한 사회문화적 정체성을 학습에 긍정적인 자원으로 바라보고자 하며, 이를 억압하는 과학수업의 문화나 사회에서의 담론을 비판함으로써 과학 학습에 있어 정당한 학습자(legitimate learner)의 범위를 확장하는데 기여해 오고 있음을 알 수 있었다. 연구들은 특히 과학교육에서 공정성(equity) 이슈를 강조하며, 전통적인 과학수업에서 소외되어 온 다양한 학습자들을 포용하고 행위주체성의 발달을 촉진하고자 하였다. 이러한 분석 결과를 바탕으로 앞으로 우리나라에서 '모두를 위한 과학교육'의 실현을 위해서는 과학 학습의 참여와 소외 현상을 둘러싼 다양한 사회문화적 기제에 대한 연구의 필요성을 주장하였고 이와 관련된 연구의 방향을 제안하였다.

정밀영양: 개인 간 대사 다양성을 이해하기 위한 접근 (Precision nutrition: approach for understanding intra-individual biological variation)

  • 김양하
    • Journal of Nutrition and Health
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    • 제55권1호
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    • pp.1-9
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    • 2022
  • In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

Importance of Lecturer's Role in Management Education

  • Viet Xuan TRINH;Duyen Thi Kim NGUYEN;Dat Ngoc NGUYEN;Loc Xuan TRAN;Huong Thi Lan PHAM
    • 유통과학연구
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    • 제22권1호
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    • pp.69-78
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    • 2024
  • Purpose: This study is undertaken from the standpoint of student-centered learning and theoretical paradigms that have developed in the business world and display conceptual affinities: the transfer of knowledge and training. Research design, data and methodology: Utilizing questionnaire surveys and multivariate data analysis are two research methodologies (CFA, SEM). Around 201 undergraduate students who were studying in Vietnam provided the data. Results: The results show importance of the faculty role in students' knowledge acquisition. The findings show that Ability to form a good relationship positively influences the development of competence. Additionally, neither ability to develop a good relationship nor learning drive or knowledge acquisition are significantly correlated with one another. The growth of competencies is positively impacted by the suitability of teaching approaches. Knowledge acquisition is favorably impacted by learning motivation, and knowledge acquisition in turn is positively impacted by competence development. Conclusions: Research has shown the important role of lecturers in students' knowledge acquisition. From this result, some implications related to lecturers are also given to help improve students' ability to acquire knowledge. Building good relationships with students (ready to answer questions, positive relationships) and good expertise will help increase learning motivation, ability to acquire knowledge as well as improve development for students.