• 수산해양교육연구
• /
• 제28권6호
• /
• pp.1894-1906
• /
• 2016

The purpose of this study is to analyze the setting and directions of cross-curricular learning topics based on research on experts' recognition of cross-curricular learning topics. The study method adopted was Delphi, and the subjects selected were curricular experts. This study has drawn following results: first, regarding the essence and problems of cross-curricular learning topics, even among the experts, there is no opinion agreed about cross-curricular learning topics' concept, essence, or characters. Second, more detailed discussion is demanded to select cross-curricular learning topics and set up a guideline about the operation. Third, it is needed to examine closely if presently suggested cross-curricular learning topics are duplicated or not and consider related subjects connected with those cross-curricular learning topics to improve education more systematically. Fourth, it is necessary to conduct more profound and systematic research on core competence that can embrace those cross-curricular learning topics. Fifth, to cope with changes in society and demands at school, it is needed to discuss how cross-curricular learning topics should be added or which learning topics should be added.

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제38권1호
• /
• pp.27-48
• /
• 2024

미래 사회의 변화에 대비하기 위해 범교과 학습이 강조되며, 범교과 학습 주제와 교과를 연계할 필요성이 증가하고 있다. 그러나 수학과에서 범교과 학습을 어떻게 다뤄야 하는지에 관한 연구는 거의 없다. 본 연구에서는 교과별 교육 과정과 수학과 교과서에 적용된 범교과 학습 주제의 내용과 방법을 문헌 분석하였다. 연구 결과 교과별 교육과정을 적용된 범교과 학습 주제의 다양성, 주된 범교과 학습 주제의 유무에 따라 총 4가지 유형으로 구분할 수 있었고 이 때 수학과 교육과정은 일부 범교과 학습 주제가 소극적으로 다뤄지고 주된 주제가 없는 유형에 속하였다. 한편, 2015 개정 교육과정에 따른 5~6학년군 수학 교과서 10종을 분석한 결과, 교육과정과 달리 교과서에는 다양한 범교과 학습주제가 적용되어 있었고 이중 환경·지속가능발전 교육, 안전·건강 교육, 진로 교육, 인성 교육, 경제·금융 교육 순으로 주를 이뤘다. 또한 수학 교과서에는 범교과 학습 주제가 소재, 발문, 설명, 자료, 삽화와 같이 다양한 유형으로 반영되어 있었는데, 주로 소재나 삽화로 나타난 경우가 많았다. 그리고 수학 교과서에는 범교과 학습 주제가 주로 도입 차시, 본 차시의 도입 활동, 특화 차시에 반영되어 있었다. 이런 결과를 바탕으로 수학과에 범교과 학습 주제를 연계하는 방안을 모색하고 제시하였다.

• 한국컴퓨터정보학회논문지
• /
• 제28권10호
• /
• pp.55-65
• /
• 2023

본 논문에서는 딥러닝 및 강화학습 연구에 대해 KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) 알고리즘 기반의 토픽 추출 및 토픽 출현 빈도 분석으로 급변하는 딥러닝 관련 연구 동향 분석을 파악하고자 한다. 딥러닝 알고리즘과 강화학습에 대한 논문초록을 크롤링하여 전반기와 후반기로 나누고, 전처리를 진행한 후 KeyBERT를 사용해 토픽을 추출한다. 그 후 토픽 출현 빈도로 동향 변화에 대해 분석한다. 분석된 알고리즘 모두 전반기와 후반기에 대한 뚜렷한 동향 변화가 나타났으며, 전반기에 비해 후반기에 들어 어느 주제에 대한 연구가 활발한지 확인할 수 있었다. 이는 KeyBERT를 활용한 토픽 추출 후 출현 빈도 분석으로 연구 동향변화 분석이 가능함을 보였으며, 타 분야의 연구 동향 분석에도 활용 가능할 것으로 예상한다. 또한 딥러닝의 동향을 제공함으로써 향후 딥러닝의 발전 방향에 대한 통찰력을 제공하며, 최근 주목 받는 연구 주제를 알 수 있게 하여 연구 주제 및 방법 선정에 직접적인 도움을 준다.

• 한국초등과학교육학회지:초등과학교육
• /
• 제31권4호
• /
• pp.541-557
• /
• 2012

Interest is acknowledged to be a critical motivational variable that influences learning and achievement. The purpose of this study was to investigate the interest of the elementary students when free inquiry activities were performed through a brain-based evolutionary scientific teaching and learning model. For this study, 106 fifth grade students were chosen and performed individually free inquiry activities. The results of this study were as follows: First, after free inquiry activities, as to free inquiry science related attitude, a statistically significant difference was not observed. But they came to have positive feelings about the free inquiry. Especially students marked higher mean score in openness showed consistency in sub-areas of free inquiry science related attitude. Second, students had interests in various fields, especially they had many interests in area of biology. They chose inquiry subjects that seems to be easily accessible from surrounding and as an important criterion of free inquiry they thought the possibility that they could successfully perform it. And students who belong to the high level in the science related attitudes and academic achievement diversified more topics. Third, most of students failed to further their topics. However, the students who specifically and clearly extended their topics suggested appropriate variables in their topics. On the other hand, students who couldn't elaborate their topics were also failed to suggest further topics and their performance of inquiry was more incomplete. In conclusion, the experiences of success in free inquiry make the science attitude of students more positive and help them extend their inquiry. These results have fundamental implications for the authentic science inquiry in the elementary schools and for the further research.

• 한국초등과학교육학회지:초등과학교육
• /
• 제29권4호
• /
• pp.474-483
• /
• 2010

In this study, we investigated preservice elementary school teachers' awareness of students' misconceptions about several science topics, and the variables influencing their awareness. Seniors (N=106) from an university of education were asked to predict elementary school students' misconceptions on science topics such as phase changes and dissolution. Their conceptions about teaching and learning were also measured. The results indicated that the preservice teachers' predictions about the kinds and/or the ratios of students' misconceptions were different from those reported in previous studies. The low level preservice teachers in terms of the degrees of possessing traditional conception about teaching and learning predicted more students' common misconceptions. The degrees of preservice teachers' constructivist conception about teaching and learning and their major, however, did not significantly influence the numbers of common misconceptions predicted.

• Journal of Information Processing Systems
• /
• 제18권1호
• /
• pp.130-145
• /
• 2022

Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

• 의학교육논단
• /
• 제18권3호
• /
• pp.167-173
• /
• 2016

This study analyzed participation experiences in a voluntarily learning community using both quantitative and qualitative methods. Sixty freshmen and sophomore medical school students in 10 learning communities participated in the study. At the time of the survey, learning communities had been operating for 10 weeks and had weekly in-person meetings. Satisfaction questionnaires and reflective essays were given and analyzed. The results showed that learning community experiences were effective in promoting students' learning motivation, cooperative learning, responsibility, and communication skills. Three essential topics and nine subjects were analyzed in the reflective essays. Three essential topics were conflict with each other due to the difference, forming deep relationships, and sharing and learning together with an in-depth study. The results of this study will contribute to collaborative learning culture and the development of learning communities in medical schools.

• 디지털산업정보학회논문지
• /
• 제15권2호
• /
• pp.19-28
• /
• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

• 의학교육논단
• /
• 제19권3호
• /
• pp.158-168
• /
• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Journal of Information Processing Systems
• /
• 제17권6호
• /
• pp.1057-1070
• /
• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.