• Communications of Mathematical Education
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• v.37 no.3
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• pp.349-368
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• 2023

The revised 2022 educational curriculum highlighted the significance of mathematical literacy as a foundational competency that can be cultivated through the learning of various subjects, along with language proficiency and digital literacy. However, due to the lack of a precise definition for mathematical literacy, there exists a challenge in systematically implementing it across all subjects in the educational curriculum. The aim of this study is to clarify the definition of mathematical literacy in the curriculum through a literature review and to analyze the application patterns of mathematical literacy in other subjects so that mathematical literacy can be systematically applied as a basic literacy in Korea's curriculum. To achieve this, the study first clarifies and categorizes the meaning of mathematical literacy through a comparative analysis of terms such as numeracy and mathematical competence via a literature review. Subsequently, the study compares the categories of mathematical literacy identified in both domestic and international educational curricula and analyzes the application of mathematical literacy in the education curriculum of New South Wales (NSW), Australia, where mathematical literacy is reflected in the achievement standards across various subjects. It is expected that understanding each property by subdividing the meaning of mathematical literacy and examining the application modality to the curriculum will help construct a curriculum that reflects mathematical literacy in subjects other than mathematics.

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.263-272
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• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.136-147
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• 2024

It has recently be emphasized in science education that lessons that can develop "scientific participation and action" should be implemented to scientifically recognize various problems and respond to them as well as risks that occur in real life. This study aims to analyze the characteristics of scientific participation and action lessons as perceived by the preservice primary school teachers. To do that, the researchers collected and analyzed the lesson plans designed by the preservice teachers based on the achievement standard related to noise for grades 3-4 in 2022 revised science curriculum. Focusing on the stages of "problem recognition," "data collection and analysis," and "implementation and sharing," the results identity the four main characteristics as problem-solving activity, inquiry activity, investigative activity, and activity that encourages practical actions. The two or three features were found to be combinated in a lesson depending on its context. In some cases, only one feature was seen in a lesson. Based on the results, educational implications were discussed in terms of the teaching and learning methods and teacher education for implementing scientific participation and action.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.170-184
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• 2024

This study focused on the practical research needed to improve elementary school science lesson plans. Specifically, a video clip-based pedagogical reasoning activity that included elementary student misconceptions was presented and the influences of this activity on preservice teachers' science lesson planning were assessed. First, the eight preservice teacher participants were asked to write a lesson plan for a dissolution and solution unit, after which a first semi-structured interview was conducted. Then, the participants participated in a video clip-based pedagogical reasoning activity. Based on the activity results, the participants revised their previously planned lessons, and second semi-structured interviews were conducted. The data from the preservice teachers' lesson plans and interview transcripts were analyzed using a constant comparative method to investigate the lesson plan changes. It was found that after the video clip-based pedagogical reasoning activity, the preservice teacher tightened the activity or changed the material to understand the students' thinking processes. In addition, they supplemented their goals and assessment criteria to accommodate the diverse students' thinking. Some also specified motivational strategies that considered student interests, motivation, and possible misconceptions. However, some preservice teachers still set goals that did not sufficiently account for student misconceptions and some planned the student assessments based only on the learning goals rather than the students' thinking. The few preservice teachers were able to develop motivational strategies that considered interest, motivation, and misconceptions. The preservice teachers claimed that they had difficulty predicting the misconceptions and connecting these to the lesson content. Discussions were then held to assist the preservice teachers to consider possible student misconceptions when planning their lessons.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.40-48
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• 2006

The purpose of this study was to investigate 6th grade pupil's thoughts during classification activities. Two suitable tools in classification activity achievement were developed to achieve this purpose. The first was an artificial stimulus card in which the attribute was prominent; and the other a natural stimulus card in which the attribute was less prominent. Participants of the study were 8 6th grade pupils from D elementary school in Yeongdeungpo-gu, Seoul. Data were collected from interviews with the pupils, the pupil's recordings of classification, the investigator's observation of pupil's actions, and video recordings of the pupil's subject classification process. Results found in this study were as following. First, when doing classification 6th grade pupils considered attribute observation, attribute estimation, preliminary inspection, criteria selection, and sample identification. Second, 6th grade pupil classification thought process was found to be repetitive, passing through the steps of attribute observation, attribute estimation, preliminary inspection, criteria selection, and lastly, sample identification. Third, 6th grade pupils took advantage of cognitive economic efficiency. Study findings also revealed guidance for the teaching and learning of scientific classification. First, once teachers understand the classification thought process of students, more effective classification guidance will be possible. Second, it is necessary that guidance fit each step of the classification thought process.