• Hwankyungkyoyuk
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• v.12 no.1
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• pp.40-63
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• 1999

As the environmental problems are recognized as daily problems in our lives, not as issues of those who are engaged in specific professional fields, the interest in environmental education is increasing gradually. The environmental education is the one that studies the environment and its problems and seeks the solutions for them. This paper deals with how the Korean subject will include environmental education. The environmental problems are already dealt with in the Korean textbooks according to the 6th curriculum for the Korean subject. A noticeable fact is that those textbooks connect the skills for language skills with environmental education. That is, the textbooks try to improve 4 language skills (speaking, listening, reading, and writing) with the Korean data related to environment, which can be the most practical means. This tendency will be also reflected in the 7th curriculum for the Korean subject, and the means will be taken by which environmental education will be able to be implemented more effectively through a variety of learning activities. In case of speaking and listening, learning activities such as speaking of, listening to, or discussing the contents concerning environmental problems can be recommended. In case of reading and literature, learning activities such as reading articles or works concerning environmental problems. Through these learning activities the Korean education will be able to achieve the goal in the fields of knowledge, information, and autonomy or attitudes which are the goals of environmental education. If the contents of the Korean curriculum are described in detail, it can be known that the Korean subject have someting to do with knowledge, skills, and recognition more deeply. In the methods In obtain information and knowledge, it will be desirable to recognize knowledge and information indirectly through various reading data rather than to recognize knowledge and information directly. Or it will be desirable to increase the sensitivity about environmental problems through literary works. For this environmental education in the above, we need to utilize discussion or presentation-oriented leaching and learning in the Korean education. Also we need to approach environmental problems by using various teaching media. We need to emphasize the education in the affective domain, especially through expression of emotions. guidance of reactions, internalization, personification, and so on.

• Journal of Engineering Education Research
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• v.15 no.1
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• pp.9-17
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• 2012

Communication skills, such as writing documents and presenting technical details, are thought to be essential for modern engineers. Engineering students usually have less interest in writing and presentation than their humanities student counterparts and their concerns are different, too. So it is necessary to teach them in a different way which is suited to them. However, teaching such subjects has been a tricky problem for educators in terms of teaching methods. Also, to teach writing and presentation effectively for engineering students, collaboration between the faculties of different disciplines is necessary because each discipline has its own specific domain knowledge and approaches. But many unsolved problems exist with regard to how to deal with the technical and administrative aspects, and so on. This paper introduces one case in the education of technical writing and presentation, which is a collaboration between an engineering faculty and a faculty of literature. The literature faculty conducts basic education and training for writing skills, while the engineering faculty teaches the technical aspects of documentation, as well as presentation skills. The focus is placed on topics such as self-introduction, searching technical literature and materials, describing and explaining things and presentation practice, etc. During the class there is cooperation in each topic domain, while the faculties collaborate in teaching and evaluation.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

• Genomics & Informatics
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• v.17 no.2
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• pp.20.1-20.5
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• 2019

Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.

• The Journal of Information Systems
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• v.33 no.1
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• pp.207-226
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• 2024

Purpose This study aims to develop a job model for data convergent talents to meet the rapidly changing demands of the data industry. To create a job model, we first define and categorize data convergent talents with balanced competencies in data technology and domain knowledge, and then develop a job model by investigating job areas, scope, activities, and competencies. Design/methodology/approach The research is conducted using the following procedures and methodology. First, we conduct a current status survey on data talent demand, data talent policies, data talent programs, and curricula at home and abroad; second, we collect opinions on the jobs and competencies required for data convergent talents and curricula for talent development through in-depth interview with experts; and third, we present the job areas and job activities of data convergent talents derived from the previous status survey and expert opinions based on the National Competency Standards(NCS). Findings The research findings indicate that there are total of six job roles for data convergent talents, including data scientist, data planner, data architect, data developer, data engineer, and data analyst. It was observed that each of these roles requires the development of common competencies within their respective fields, followed by a need for further specialization into specific competencies within each professional domain.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.39-46
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• 2011

Clustering is one of the well-known unsupervised learning methods, in which a data set is grouped into some number of homogeneous clusters. There are numerous clustering algorithms available and they have been used in various applications. Fuzzy c-means (FCM), the most well-known partitional clustering algorithm, was established in 1970's and still in use. However, there are some unsolved problems in FCM and variants of FCM are still under development. In this paper, the problems in FCM are first explained and the available solutions are investigated, which is aimed to give researchers some possible ways of future research. Most of the FCM variants try to solve the problems using domain knowledge specific to a given problem. However, in this paper, we try to give general solutions without using any domain knowledge. Although there are more things left than discovered, this paper may be a good starting point for researchers newly entered into a clustering area.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.241-245
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• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.3
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• pp.5-27
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• 2017

Retrieval of scholarly articles about a specific research issue is a routine job of researchers to cross-validate the evidence about the issue. Two articles that focus on a research issue should share similar terms in their core contents, including their goals, backgrounds, and conclusions. In this paper, we present a technique CCSE ($\underline{C}ore$ $\underline{C}ontent$ $\underline{S}imilarity$ $\underline{E}stimation$) that, given an article a, recommends those articles that share similar core content terms with a. CCSE works on titles and abstracts of articles, which are publicly available. It estimates and integrates three kinds of similarity: goal similarity, background similarity, and conclusion similarity. Empirical evaluation shows that CCSE performs significantly better than several state-of-the-art techniques in recommending those biomedical articles that are judged (by domain experts) to be the ones whose core contents focus on the same research issues. CCSE works for those articles that present research background followed by main results and discussion, and hence it may be used to support the identification of the closely related evidence already published in these articles, even when only titles and abstracts of the articles are available.

• Journal of Engineering Education Research
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• v.25 no.1
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• pp.33-45
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• 2022

This study aims to investigate effects of scaffolding on writing apprehension and media literacy in engineering freshmen's synchronous online writing course, and the relationships between the two variables. 'Scaffolding' is in-time support provided by a teacher/tutor or competent peer that enables students to meaningfully gain skills at problem solving process. Also, it is one of the most frequently mentioned concepts in education as well as one of the more necessary teaching strategies in an online writing course. In this study, provided treatments for the experiment were supportive scaffolding for domain-specific knowledge and reflective scaffolding for meta-cognitive knowledge. Participants were 102 engineering undergraduate students, who were assigned to two experimental groups by scaffolding types. A process-based writing course in online learning environment was conducted for 8 weeks. The writing tasks were given according to writing process. The findings were that, firstly, there were statistically significant writing apprehension's reduction and self-expression's improvement through the scaffolding provided in writing class. Secondly, writing apprehension's reduction and self-expression's improvement were significant in supportive scaffolding group. Thirdly, media literacy predicted writing apprehension. The practical implications of these findings are discussed herein, with particular attention on ways for writing apprehension's reduction as well as media literacy's enhancement.