• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.193-198
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• 2023

This paper analyzed the validity of exercise problems for each unit in Python programming education. Practice questions presented for each unit are presented through an online learning system, and each student uploads an answer code and is automatically graded. Data such as students' mid-term exam scores, final exam scores, and practice questions scores for each unit are collected through Python lecture that lasts for one semester. Through the collected data, it is possible to improve the exercise problems for each unit by analyzing the validity of the automatic scoring exercise problems. In this paper, Orange machine learning tool was used to analyze the validity of automatic scoring exercises. The data collected in the Python subject are analyzed and compared comprehensively by total, top, and bottom groups. From the prediction accuracy of the machine learning model that predicts the student's final grade from the Python unit-by-unit practice problem scores, the validity of the automatic scoring exercises for each unit was analyzed.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.139-149
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• 2022

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

• Water for future
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• v.55 no.11
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• pp.62-72
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• 2022

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.1-6
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• 2021

This study designed an artificial intelligence education program using localized public data as an educational method for improving computational thinking in elementary school students. Program design and development was carried out based on the results of pre-requisite analysis on elementary school students according to the ADDIE model. Based on localized public data, the program was organized to learn the principles of artificial intelligence by utilizing "Machine Learning for Kids" and "Scratch" and to solve problems and improve computational thinking skills through abstracting public data for purpose.Through subsequent research, it is necessary to put this education program into the field and verify the change in students' computational thinking as a result.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.105-118
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• 2023

In this study, we predicted the presence of fog with a one-hour delay using the XGBoost DART machine learning algorithm for Andong, which had the highest occurrence of fog among inland stations from 2016 to 2020. We used six datasets: meteorological data, agricultural observation data, additional derived data, and their expanded data. The weather phenomenon numbers obtained through naked-eye observations and the visibility distances measured by visibility meters were classified as fog [1] or no-fog [0]. We set up twelve machine learning modeling experiments and used data from 2021 for model validation. We mainly evaluated model performance using recall and AUC-ROC, considering the harmful effects of fog on society and local communities. The combination of oversampled meteorological data features and the target induced by weather phenomenon numbers showed the best performance. This result highlights the importance of naked-eye observations in predicting fog using machine learning algorithms.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.53-64
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• 2020

As online learning forms centered on video lectures become more common and constantly increasing, the video-based learning environment applying various educational methods is also changing and developing to enhance learning effectiveness. Learner's log data has emerged for measuring the effectiveness of education in the online learning environment, and various analysis methods of log data are important for learner's customized learning prescriptions. To this end, the study analyzed learner behavior data and predictions of achievement by machine learning in video-based learning environments. As a result, interactive behaviors such as video navigation and comment writing, and learner-led learning behaviors predicted achievement in common in each model. Based on the results, the study provided implications for the design of the video learning environment.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.291-296
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• 2024

This study analyzed various machine learning models that predict employment outcomes after vocational training using pre-assessment data of disabled vocational trainees. The study selected and utilized the most appropriate machine learning models based on a data set containing various personal characteristics, including trainees' gender, age, and type of disability. Through this analysis, the goal is to improve the employment rate and job satisfaction of disabled trainees using only pre-assessment data. As a result, it presents a universal approach that can be applied not only to people with disabilities, but also to vocational trainees from a variety of backgrounds. This is expected to make an important contribution to the development and implementation of tailored vocational training programs, ultimately helping to achieve better employment outcomes and job satisfaction.

• The Mathematical Education
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• v.59 no.4
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• pp.373-387
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• 2020

The purpose of this study is to verify the effects of personalized learning platforms applied with machine learning recommendation modules that upgrade recommended algorithms by themselves through learning big data analysis on students' learning time, self-directed learning ability, mathematics achievement, and attitudes toward mathematics, and the correlation between them. According to the study, customized learning affected learning time, self-directed learning ability and mathematics attitude, while learning time affected self-directed learning ability. Self-directed learning ability has had a significant impact on the attitude of mathematics and mathematical achievements. As a result of the mediated effectiveness test, the indirect impact of customized learning on mathematics attitude and mathematics performance was significant through the medium of learning time and self-directed learning ability.