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일학습병행제 학습근로자의 교육훈련 만족도와 업무몰입, 이직의도의 구조적 관계에 대한 연구
강원석,김우철 중앙대학교 한국인적자원개발전략연구소 2019 역량개발학습연구 Vol.14 No.1
This study aims to identify the relationship between the satisfaction with training, work engagement, and turnover intention from the various aspects of the work and learning in a dual system. The study found that the satisfaction of learning workers who participated in education and training programs through the work and learning dual system shows a statistically significant static(+) effect on the work engagement, and statically significant negative(-) effects on turnover intention. The result proved that the statistically significant effect on work engagement resulted from the work and learning dual system which also made the statistically significant negative(-) effect on the turnover intention. The mediated effect of the work engagement between the satisfaction with training and training and the turnover intention of learning workers was a statistically significant static(+) effect. In conclusion, this paper presented academic and practical implications drawn based on the result of research and also stated suggestions regarding future research. 본 연구의 목적은 교육훈련 만족도와 업무몰입, 이직의도의 관계를 일학습병행제의 관점에서 실시한 연구가 매우 부족한 실정에서 각각의 관계를 규명하고, 이를 통해 기업의 일학습병행제 참여를 증가시켜 개인과 조직의 성과를 증가시키고 이직률을 감소시키는 등 소기의 목적 달성을 위한 실증연구를 제공하는데 있다. 이를 위해 일학습병행제에 참여한 학습근로자 340명을 대상으로 교육훈련 만족도와 업무몰입, 이직의도와 관련하여 설문조사를 실시하여 구조적 관계를 확인하였다. 연구 결과 일학습병행제를 통해 받은 교육훈련에 대한 학습근로자들의 만족감이 업무몰입에 통계적으로 유의한 정적(+) 영향을 미치는 것으로 확인되었으며, 일학습병행제 참여를 통해 실시한 교육훈련에 대한 학습근로자들의 만족감은 이직의도에 통계적으로 유의한 부적(-) 영향을 미치는 것으로 확인되었다. 또한 일학습병행제 참여를 통한 업무몰입은 이직의도에 통계적으로 유의한 부적(-) 영향을 미치는 것으로 확인되었으며, 학습근로자의 교육훈련 만족도와 이직의도 사이에서 업무몰입의 매개효과는 통계적으로 유의미한 것으로 검정되었다. 마지막으로 연구 결과를 토대로 학문적, 실천적 시사점을 제시하고 향후 연구에 대해 제언하였다.
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강원석 安東大學 1996 安東大學 論文集 Vol.18 No.1
A natural language system needs the dictionary for analysis of natural language. Because the dictionary effects output of the system, we must design the good dictionary. However, the designed dictionary may not be used to other domain or systems. In this thesis, we design a large scale thesaurus prototype which can be used to other domain or systems with a little transformation. This thesaurus enables to extract the super and sub-concept of the given concept. It makes it easy to construct the Korean processing system by providing the information used in the system.
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강원석 安東大學校 工業技術硏究所 1997 공업기술연구논문집 Vol.4 No.1
In this thesis, we propose a concept-based text categorization system(TAXON) of probability-vector method with thesaurus. TAXON consists of a Korean morphological analyzer, thesaurus tool, and similarity analyzer. The similarity analyzer decides the subject of an input document using probability-vector method. The thesaurus tool makes the system get the concepts of input words. The Korean analyzer acquires the words with noun category from input documents. TAXON is the real-time concept-based text categorization system with expandability.
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강원석 安東大學校 工業技術硏究所 1996 공업기술연구논문집 Vol.3 No.1
In this paper, we suggest a hybrid system which consists of a neural network, a sematic feature generator, and a rule-based controller to solve the PP(Prepositional Phrase)-attachment problem. The hybrid system operates in consideration of semantic information, crossing constraint, and local constraint. The neural network determines the preferential PP-attachment by comparing two attachment possibilities. This system automatically generates the semantic features which is the input of the neural network. The controller manages the excution of the neural network and the semantic feature generator to choose a PP-attachment among many attachment possibilities and enables PP-attachment resolution to variable sized input sentences.
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강원석 安東大學 1995 安東大學 論文集 Vol.17 No.1
In a machine translation system, semantic features are used to distinguish the meaning of a word. The definition and acquisition of the semantic features has important effect on the quality of the machine translation system. This paper proposes a semantic feature set and an automatic method of generating semantic features. The semantic feature set is designed with a basis on the characteristics of English and Korean. The semantic features of an input word are generated by the automatic feature generator which is designed with a basis on the word sense hierarchy built in the Word Net. The proposed semantic feature set and the generator will contribute to natural language analysis as well as EKMT.
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강원석 安東大學校 工業技術硏究所 1999 공업기술연구논문집 Vol.6 No.1
In this paper, we design and implement a categorization system to the web-document which is diverse and noisy. As there is no consistent form and content in the document, it is not easy to design and implement the web-document categorization system. The presented system adopts the neural network method which is suitable for learning and processing the noise to determine the category of the web-document. This system consists of a Korean morphological analyzer, a sense extractor, a sense disambiguator, and a category determiner. The morphological analyzer separates the noun word in the document. The sense extractor acquires the senses on the words in the document. The sense disambiguator solves the ambiguity of the word. Finally, the category determiner decides the category of the input document with the neural network. In this paper, we use the sense disambiguator to solve the ambiguity of the word. Therefore, we can get the more good categorization quality with the sense disambiguator.
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가중치 적용과 개선된 시소러스 도구에 의한 개념 기반 문서분류기 TAXON의 개선
강원석 安東大學校 工業技術硏究所 1998 공업기술연구논문집 Vol.5 No.1
In this paper, we present the improved concept-based text categorization system(TAXON) with probability-based method. The system consists of a Korean morphological, analyzer, a thesaurus tool, a weight determinator, and a similarity measurer. The thesaurus tool finds the concepts of the terms extracted by the Korean analyzer. The weight determinator decides the weight of the concepts with the heuristic rules. Finally, the similarity measurer decides the category of the input text with comparing the concept-probability vectors of the input text and categories. In this paper, we use the heuristic rules to acquire the concept-probability vector included the weight on each concept, and use the improved thesaurus tool to get the concepts to more terms than ever. Therefore, we can get the more good categorization quality with the heuristic rules and the improved thesaurus tool.
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