• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.233-234
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• 2022

본 논문에서는 효율적인 AI 교육을 위한 데이터셋 아카이브와 데이터 활용을 위한 프로그래밍 플랫폼과의 연동 모듈을 제안한다. 데이터셋 아카이브는 공공데이터를 전처리하여 생성한 데이터를 모아 설계하며, 프로그래밍 플랫폼 코드비(CodeB)와 연동하여 데이터를 활용할 수 있도록 한다. 코드비(CodeB)는 파이썬 블록 프로그래밍 플랫폼으로 연동을 통해 데이터를 활용한 프로그래밍이 가능하다.

• Smart Media Journal
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• v.13 no.2
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• pp.44-51
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• 2024

The construction of a stress measurement detaset plays a curcial role in various modern applications. In particular, for the efficient training of artificial intelligence models for stress measurement, it is essential to compare various biases and construct a quality-controlled dataset. In this paper, we propose the construction of a stress measurement dataset with quality management through the comparison of various biases. To achieve this, we introduce strss definitions and measurement tools, the process of building an artificial intelligence stress dataset, strategies to overcome biases for quality improvement, and considerations for stress data collection. Specifically, to manage dataset quality, we discuss various biases such as selection bias, measurement bias, causal bias, confirmation bias, and artificial intelligence bias that may arise during stress data collection. Through this paper, we aim to systematically understand considerations for stress data collection and various biases that may occur during the construction of a stress dataset, contributing to the construction of a dataset with guaranteed quality by overcoming these biases.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.439-448
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• 2022

This study aims to search for education-related datasets provided by public data portals and examine what data types are constructed through classification using topic modeling methods. Regarding the data of the public data portal, 3,072 cases of file data in the education field were collected based on the classification system. Text mining analysis was performed using the LDA-based topic modeling method with stopword processing and data pre-processing for each dataset. Program information and student-supporting notifications were usually provided in the pre-classified dataset for education from the data portal. On the other hand, the characteristics of educational programs and supporting information for the disabled, parents, the elderly, and children through the perspective of lifelong education were generally indicated in the dataset collected by searching for education. The results of data analysis through this study show that providing sufficient educational information through the public data portal would be better to help the students' data science-based decision-making and problem-solving skills.

• Journal of The Korean Association of Information Education
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• v.26 no.3
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• pp.197-207
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• 2022

In this paper, we develop a machine learning based block code generation and recommendation model for the purpose of reducing cognitive load of learners during coding education that learns the learner's block that has been made in the block programming environment using natural processing model and fine-tuning and then generates and recommends the selectable blocks for the next step. To develop the model, the training dataset was produced by pre-processing 50 block codes that were on the popular block programming language web site 'Entry'. Also, after dividing the pre-processed blocks into training dataset, verification dataset and test dataset, we developed a model that generates block codes based on LSTM, Seq2Seq, and GPT-2 model. In the results of the performance evaluation of the developed model, GPT-2 showed a higher performance than the LSTM and Seq2Seq model in the BLEU and ROUGE scores which measure sentence similarity. The data results generated through the GPT-2 model, show that the performance was relatively similar in the BLEU and ROUGE scores except for the case where the number of blocks was 1 or 17.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.221-226
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• 2021

Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.35-44
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students' self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.183-188
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• 2021

Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.