• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.175-178
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• 2005

스마트 홈과 같은 유비쿼터스 환경은 다양한 센서 및 제어 네트워크가 밀집되어 있는 복잡한 시스템이다. 본 논문에서는 이러한 환경하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)를 줄이고 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 사용자 행동 패턴 선호도 학습 기법을 제안한다. 이를 위해 지식 발견(Knowledge Discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지 귀납(Fuzzy Inductive)학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성있는(consisitent) 퍼지 상관 룰(fuzzy association rule)을 얻어내도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.619-626
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• 2007

Constructive induction is a technique to draw useful attributes from given primitive attributes to classify given examples more efficiently. Useful attributes are obtained from given primitive attributes by applying appropriate operators to them. The paper proposes a constructive induction approach for a GA-based inductive learning environment that learns classification rules that ate similar to rules used in PROSPECTOR from given examples. The paper explains our constructive induction approach in details, centering on operators to combine primitive attributes and methods to evaluate the usefulness of derived attributes, and presents the results of various experiments performed to evaluate the effect of our constructive induction approach on the GA-based learning environment.

• Asia Pacific Journal of Corpus Research
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• v.4 no.1
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• pp.61-71
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• 2023

This study examines the effects of data-driven learning (DDL)-an approach employing corpora for inductive language pattern learning-on error identification in second language (L2) writing. The data consists of error identification instances from fifty-five participants, compared across different reference materials: the Corpus of Contemporary American English (COCA), dictionaries, and no use of reference materials. There are three significant findings. First, the use of COCA effectively identified collocational and form-related errors due to inductive inference drawn from multiple example sentences. Secondly, dictionaries were beneficial for identifying lexical errors, where providing meaning information was helpful. Finally, the participants often employed a strategic approach, identifying many simple errors without reference materials. However, while maximizing error identification, this strategy also led to mislabeling correct expressions as errors. The author has concluded that the strategic selection of reference materials can significantly enhance the effectiveness of error identification in L2 writing. The use of a corpus offers advantages such as easy access to target phrases and frequency information-features especially useful given that most errors were collocational and form-related. The findings suggest that teachers should guide learners to effectively use appropriate reference materials to identify errors based on error types.

• Journal of Korean Clinical Health Science
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• v.12 no.1
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• pp.1678-1688
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• 2024

Purpose: This study was conducted to explore the experiences of nursing students who participated in the pneumonia and pleural effusion using web-based virtual reality and high-fidelity simulation. Methods: This study is qualitative study using inductive content analysis. We developed simulation scenario regarding pneumonia and pleural effusion. Eleven nursing students who participated in simulation were interviewed between June 20 to August 25, 2022. The interviews were transcribed and analyzed according to the inductive content analysis. Results: The results were analyzed into three key categories: 'pre-learning and psychological burden before simulation','increased learning satisfaction','improved clinical performance'. Conclusions: Participants was able to integrate their previous experience, including clinical practice experiences, web-based virtual simulation, into high-fidelity simulation and effectively enhanced their learning experience. Therefore, when providing various types of simulation simultaneously, it is necessary to take into account the prior students' experiences and to organize simulation education by considering the characteristics of simulation.

• Educational Technology International
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• v.24 no.2
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• Educational Technology International
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• v.11 no.2
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• pp.149-172
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• 2010

Knowledge can be more meaningful when it is shaped and personalized through interaction with others. Implementation of open learning environments such as open courseware or shared knowledge communities has gradually become more common. A case study which investigated instructors' experiences and perceptions of publishing and using open courseware in the classroom was conducted at a university in Korea. Responses from participating students and an evaluation group regarding how they perceived open learning environments were also examined. Based on the inductive analysis of the data, this study discusses advantages and challenges of publishing open courseware and collaborative learning environments. Also, practical guidelines for developing reusable learning materials are suggested.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.822-832
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• 2001

We have implemented an inductive learning system that learns PROSPECTOR-rule-style classification rules from sets of examples. In our a approach, a genetic algorithm is used in which a population consists of rule-sets and rule-sets generate offspring through the exchange of rules relying on genetic operators such as crossover, mutation, and inversion operators. In this paper, we describe our learning environment centering on the syntactic structure and meaning of classification rules, the structure of a population, and the implementation of genetic operators. We also present a method to evaluate the performance of rules and a heuristic approach to generate rules, which are developed to implement mutation operators more efficiently. Moreover, a method to construct a classification system using multiple learned rule-sets to enhance the performance of a classification system is also explained. The performance of our learning system is compared with other learning algorithms, such as neural networks and decision tree algorithms, using various data sets.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.30-30
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• 1987

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.261-268
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• 2017

The development of ICT technology centered on mobile smart platforms have been emerging as a shared economic platform based on collaborative consumption. In this study, we analyze what factors affect success and failure in commercialized shared economic platforms from 2008 to 2016, and present what policy factors are needed to activate shared economic platform. To do this, we analyze successful cases of shared economic platforms and failed cases, derive key variables that affect success and failure, and conduct inductive analysis based on ID 3 algorithm based on them. Through this, we present the policy factors for the commercial success of the shared economic platform by deriving the rules for the success and failure of the shared economic platform.