• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1894-1906
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• 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.

• Communications of Mathematical Education
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• v.38 no.1
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• pp.27-48
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• 2024

In order to prepare for changes in future society, cross-curricular learning is emphasized, and the need to link cross-curricular learning topics and subjects is increasing. However, there are few studies on how to deal with cross-curricular learning in mathematics education. This study analyzed the contents and methods of cross-curricular learning topics in subject-specific curriculum and mathematics textbooks. As a result of the study, the curriculum can be categorized into four types according to the variety of cross-curricular learning topics applied and the presence or absence of a main cross-curricular learning topic, and the mathematics curriculum belongs to the type where some cross-curricular learning topics are dealt with passively and there is no main topic. On the other hand, the analysis of 10 math textbooks for grades 5 and 6 according to the 2015 revised curriculum showed that, unlike the curriculum, various cross-curricular learning topics were applied in the textbooks, mainly environment and sustainable development education, safety and health education, career education, character education, and economic and financial education. In addition, in mathematics textbooks, cross-curricular learning topics appeared in various types such as materials, questions, explanations, illustrations, and in many cases, they appeared mainly as materials or illustrations. Based on these findings, implications were explored and suggested on how to integrate and apply cross-curricular learning topics in mathematics.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• Journal of Korean Elementary Science Education
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• v.31 no.4
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• pp.541-557
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• 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.

• Journal of Korean Elementary Science Education
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• v.29 no.4
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• pp.474-483
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• 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
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• v.18 no.1
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• pp.130-145
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• 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.

• Korean Medical Education Review
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• v.18 no.3
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• pp.167-173
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• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.19-28
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• 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.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 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
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• v.17 no.6
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• pp.1057-1070
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• 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.