• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.100-109
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• 2020

At the beginning of the discussion of early childhood software education, the study was conducted to apply an early childhood software education program based on computational thinking and analyze the effects of early childhood software education programs. In this study, a balanced distribution of software education, content elements and computing thinking elements was applied to achieve the ultimate goal of software education, which is to improve computing thinking. As early as possible, it's a good idea to start teaching to remind themselves how to think through experiences and play activities and to discover problems and find solutions by themselves. In the analysis results, early childhood software education program we applied affected positive impacts on software education effect, computational thinking of early childhood. Based on these results, a program was proposed for systematic early childhood software education that effective develope of computational thinking.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.499-512
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• 2024

In this study, we diagnosed the results and achievements of pilot operation of high-skilled meister course in degree-linked work-study in parallel, which has been carried out, and suggested of direction for improvement and desirable implementation. In the achievement diagnosis, the results of training served by the university operating this course, the performance and recognition of expert activities after graduation, the self-efficacy in the degree of strengthening one's capabilities as a corporate field teacher, and the self-efficacy for the degree of strengthening the company's human capabilities, and the government performance was diagnosed. Based on the analysis, various operational improvement directions were derived to revitalize high-skilled meister courses in degree-linked work-study in parallel. This study increased the reliability of the study by diagnosing and examining the empirical performance of the entire Advanced Meister Course pilot project through actual performance aggregate data and graduates who participated in the project. It also provides a theoretical basis for policy decisions by verifying implications and desirable directions for improvement through expert advice.

• Journal of Engineering Education Research
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• v.9 no.4
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• pp.71-82
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• 2006

To accomplish the educational objectives of a department, a system for CQI(Continuous Quality Improvement) is necessary. Improving the educational system by survey analysis is one of the most important factors for accomplishing the educational objectives. In general, survey analysis is carried out by using statistical distribution on an attribute or correlation analysis between two attributes. However, these analysis schemes have a limitation that they cannot find relations among various attributes. In this paper, an in-depth survey analysis method applying data mining techniques is presented. Data mining is a technique for extracting interesting knowledges from a large set of data. Survey from undergraduate students in the School of Computing of Soongsil University is analyzed in this paper by using a data mining tool, called Clementine. Results of Clementine analysis show the relationship between 'grade', and other attributes hierarchically, and provide useful information that can be applied in student consulting and program improvement.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.23-30
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• 2017

This is a study on a case applying flipped learning to coding classes that is a college liberal arts course. A required coding class for the students who do not major in computers needs a teaching method differentiated from a coding education for training experts. The present study presented a flipped learning teaching model for the coding education of non-major students, and observed its effect and possibility. Flipped learning enables learners to learn with on-line contents anywhere and anytime they want and concentrate on practice education based on what they learned during class. Accordingly, the study sought for the solution to maximize the efficiency of teaching and learning through flipped learning. A pre and post surveys after applying a flipped learning to a practical class confirmed that the students taught using flipped learning were more positively assessed in learning satisfaction than those taught using a traditional method, and that in academic achievement as well, flipped learning was more effective.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.333-338
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• 2020

This paper proposes an augmented reality-based electronic circuit education system as a way for electronic circuit education, which is the basis of ICT convergence technology field. It consists of a hardware module that can identify the actual circuit and a mobile educational content that can check the current flow, input, output, and measured value by applying augmented reality technology. An experiment was conducted on image recognition, which is the main performance, for the purpose of stable operation of the system, and as the experimental method the recognition rate was measured by changing the distance between the hardware module and the mobile device to a certain interval. As a result of the experiment, the recognition rate was 100 percent at a distance of 25[Cm] or higher, and it was confirmed that the recognition rate decreased by 12% at a distance below 25[Cm], which can be said to be the effect of an error that results in image loss taken due to close distance. In the future, we plan to apply the education system presented in this paper to classes, which increases the efficiency of classes and improve students' interest and understanding of the subject.

• 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 Practical Engineering Education
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• v.15 no.3
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• pp.597-603
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• 2023

This study evaluates the AI digital competencies of teachers and presents a step-by-step framework for teacher's AI digital competencies that can be utilized in training. To do this, AI digital competencies were analyzed from the perspective of utilization and disposition, linked with the Technological Pedagogical Content Knowledge (TPACK) perspective. Then, as a precedent for step-by-step teacher AI digital competencies, the 3-step competency of the British Education and Training Foundation and the UNESCO ICT Teacher Competency Framework were presented. In this study, teacher's AI digital competencies were divided into three stages: entry, adaptation, and leadership, considering precedent research and domestic conditions. The initial entry stage passed the validity test in the second round of the Delphi survey, and the other two stages passed in the first round. The final entry stage is described as a stage where teachers understand AI digital but have difficulty implementing it, the adaptation stage is a level applied to standard curricula, and the leadership stage is a level where AI digital is applied in advanced courses and teachers serve as models for others. Through the overall AI digital competencies presented in this study, detailed competency development is possible, and it can be used as a reference material for developing evaluation items.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.513-520
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• 2024

This study investigates the changes in teachers' roles as the impact of AI on school education expands. Traditionally, teachers have been responsible for core aspects of classroom instruction, curriculum development, assessment, and feedback. AI can automate these processes, particularly enhancing efficiency through personalized learning. AI also supports complex classroom management tasks such as student tracking, behavior detection, and group activity analysis using integrated camera and microphone systems. However, AI struggles to automate aspects of counseling and interpersonal communication, which are crucial in student life guidance. While direct conversational replacement by AI is challenging, AI can assist teachers by providing data-driven insights and pre-conversation resources. Key competencies required for teachers in the AI era include expertise in advanced instructional methods, dataset analysis, personalized learning facilitation, student and parent counseling, and AI digital literacy. Teachers should collaborate with AI to emphasize creativity, adjust personalized learning paths based on AI-generated datasets, and focus on areas less amenable to AI automation, such as individualized learning and counseling. Essential skills include AI digital literacy and proficiency in understanding and managing student data.