• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.343-346
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• 2012

학생들이 프로그래밍 교육에서 학습에 어려움을 겪고 있는 객체지향 프로그램 개발 능력을 향상시킬 수 있는 교육방법으로서 이 논문에서는 시각 프로그래밍 교육 방법론과 교과과정을 도입한다. 3D 프로그래밍 환경을 제공하여 게임 형태의 프로그램을 객체지향 프로그래밍 방식으로 쉽게 작성할 수 있는 Alice 언어를 정규 교과과정에 도입하여 학생들의 프로그래밍 언어에 대한 이해도의 향상과 프로그래밍에 대한 학습 의욕을 고취하여 능력 있는 프로그래머의 양성에 기여할 수 있을 것으로 기대된다. 시각 프로그래밍 교육 방법의 도입에 대한 성과를 분석하기 위하여 프로그래밍 교육의 유용성을 분석할 수 있는 평가 지표를 제시하고 학생들의 프로그래밍 학습 효과를 분석한다.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.2
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• pp.51-73
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• 2015

The Web now provides instant access to an unprecedented amount of information that was unthinkable even 20-30 years ago. However, the full potential of the contents available through the Internet can only be realized when one can speak and understand foreign languages, especially English which accounts for more than half of web contents. In this study, we try to investigate the effect of search task languages and task complexity on searching performance. A total of thirty students enrolled at a top private university in Korea were recruited as study subjects. We set up a quasi-experimental design in which thirty subjects are randomly assigned to a set of eight different search tasks containing an equal number of simple and complex tasks and an equal number of tasks in Korean and in English. The results show that there is a significant difference between simple and complex tasks in terms of SERP time, number of queries used, correctness of results and total search time. However, task language does not seem to have affected search performance for this study group. In addition, students with high English proficiency test scores show comparable search performance in English tasks compared with lower test scores. But we note differences in behavioral patterns (different search engines used and search tactics) among the study participants.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Journal of vocational education research
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• v.30 no.4
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• pp.69-87
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• 2011

Until now, most of the attention related information and knowledge transmission is on the verbal message provided by educators. But recently, many researchers are emphasizing importance of nonverbal communication behavior in the evaluation of communicator include educators. When nonverbal messages reinforce verbal messages, meaning is conveyed quickly and easily and with increased comprehension. The purpose of this study is to analyze the effect of professor nonverbal communication on students' academic achievement. In this study, nonverbal communication was divided into the three dimensions of kinesis, proxemics, paralanguage, and physical appearance. It was studied to examine the direct or indirect effects of nonverbal communication on attitude toward the professor and academic achievement. Additionally, it examined the moderating effect of academic self-efficacy in the relationship between attitude toward the professor and academic achievement. The data were collected from 214 college students using an online survey. The results showed that the kinesis, proxemics, and physical appearance, except paralanguage, have significantly positive influence on attitude toward the professor. And the moderating effect of academic self-efficacy has also been founded.

• Journal of the Korean earth science society
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• v.31 no.1
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• pp.71-87
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• 2010

The purpose of this study was to explore 8th-grade students' imageries of the Earth. We analyzed the middle school students' imageries about the Earth represented with words and drawings in Earth Systems Understanding (ESU, hereafter) framework. The students' imageries about 'the Earth' are vary by their experiences and prior-knowledge, which significantly impacts their imagery construction. Especially, the students' ESU were characterized into two aspects: One is a macroscopic view point based on full-objects of the Earth by indirect experiences and the other is everyday view point based on scene of the Earth surface and environment by direct experiences. Results revealed students' imageries about the Earth were impacted by visual experiences and those students' ESU were more represented by drawing as visual imagery than by words, formal language. The negative imageries were mainly represented through interactions of the Earth subsystems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.742-748
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• 2014

The research of the object-oriented visual programming education is related to investigate the instructional method for computer programming, the process for program development for students, and the suitability analysis and design methodology for program development. In order to develop the educational methodology for the students who have the difficulties to study the computer programming and to enhance the abilities for the development of object-oriented programs, in this paper, we suggest the evaluation metrics to apply and assess for the object-oriented programming education. Also, we introduce the visual programming education methodology and curriculum using Alice programming. The evaluation metrics for the visual programming education suggested in this paper, is accepted when the visual programming languages/tools are used in the programming education to assess and analyze the suitabilities for the programming education for students, so to utilize the analyses for abilities of understanding, abstraction, and program implementation by level.

• Fire Protection Technology
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• s.42
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• pp.17-25
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• 2007

쾌적하고 명료성이 높은 교실내의 음향환경은 학생들의 학업 성취도 및 교사의 언어전달 이해 향상을 이룩할 수 있는 중요한 사항이다. 이러한 우수한 교육환경을 위해서는 우선 우리나라의 교실 음향 실태 파악과 함께 교육시설의 음향상태 및 소음에 의한 학업 성취도, 심리적, 사회적 영향에 대한 정량적 조사가 이루어 져야 한다. 국내 교육시설의 음향 실태 파악을 통해 교육시설에서의 음향 및 소음 기준을 수립하여 교육시설내의 음환경 개선을 추진해야 한다. 교실 음향개선을 합리적으로 평가할 수 있는 지표로서 잔향시간과 배경소음이 가장 중요한 요소로 알려져 있다. 교실의 우수한 음환경을 위해 적절한 잔향시간은 $0.4\sim0.6$초, 배경소음은 NC-$25\sim30$수준이 확보되어야 하는 것으로 조사되었다. 또한 교사의 음성레벨과 배경소음과의 비(S/N비)는 최소 10 dB이상 되어야 강의 내용 전달이 가능하며, 각종 기계설비에서 발생되는 소음 및 진동은 저소음 기기 선정을 통해 최소화하여야 한다. 인접 실에서 발생되는 소음을 충분히 차단하기 위해서는 차음성능 우수한 구조의 벽계로 개선되어야 하며, 기밀성이 높은 출입문사용과 적절한 출입구 배치를 통해 인접한 교실로의 소음 전달을 최소화 할 수 있다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• Journal of Science Education
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• v.39 no.3
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• pp.418-433
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• 2015

In this study, we investigated the cognitive characteristics of low science achieving middle school students in K-WISC-IV, and compared the results with high science achieving and achieving students. The results showed us that high science achieving students scored higher than counterparts in FSIQ. Low science achieving students scored lower than high science achieving and achieving students in VCI. Especially low science achieving students scored lower than two groups in subtest SI. The low level of abstraction in low science achieving students is due to the lack of scientific reasoning ability. Therefore subtest SI is considered as highly discriminating test for low science achieving group. Low levels in verbal comprehension, abstraction and reasoning ability are the major factors in poor school performance. High science achieving students scored more than achieving and low achieving students in WMI. Because the working memory is involved in scientific reasoning problem solving process, it is believed to play an important role in science achieved.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.239-259
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• 2024

In this study, a qualitative meta-analysis was conducted on research papers on earth science education to derive knowledge of students' understanding of specific science topics-greenhouse effect, global warming, and climate change-within the context of collective Pedagogical Content Knowledge (PCK). Twenty-two research papers addressing students' alternative conceptions (misconceptions) about these topics were selected and analyzed for their respective definitions, causes (mechanisms), and impacts. Semantic network analysis and a mental model framework were applied to synthesize the findings. The meta-analysis revealed several key insights: (1) Regarding the greenhouse effect, students often used the terms "greenhouse effect" and "global warming" interchangeably, lacked knowledge about the types of greenhouse gases, and misunderstood their roles. They commonly associated the greenhouse effect with environmental pollution or changes in the ozone layer, failing to recognize its relation to the heat balance between the surface and atmosphere. (2) Concerning global warming, students confused it with sea level rise and linked it to pollution, ozone layer changes, and glacier melting. They understood global warming as a disruption of the heat balance between the surface and atmosphere but had misconceptions about its environmental impacts. (3) In terms of climate change, students used the term interchangeably with global warming, weather change, and climate anomalies. They associated climate change with atmospheric pollution and ozone layer depletion but misunderstood its environmental impacts. As result, three mental models-categorical, mechanistic, and hierarchical misconceptions-were identified as collective PCK. The implications for enhancing earth science teachers' PCK were discussed based on these findings.