3 research outputs found
A Study on the Development of Data Literacy Content Framework for Elementary School Students
Full text linkThis study aimed to develop a data literacy content framework for elementary school students to establish a foundation for systematic data literacy education. The research was conducted through a literature review and analysis of the 2022 Revised National Curriculum of Korea. Based on Ridsdale et al. (2015)'s framework, components of data literacy suitable for elementary students were derived, and curriculum achievement standards related to data literacy were analyzed to develop the content framework. The research identified eight key components of data literacy: understanding data, data collection, data evaluation, data organization, data analysis, data visualization, data-driven decision-making, and data ethics. Curriculum analysis revealed that science (36.3%) and social studies (32.7%) subjects contained the highest proportion of data literacy elements, with grades 5-6 (63.2%) including more achievement standards than grades 3-4 (36.8%). The developed framework is categorized into three domains: knowledge and understanding, processes and skills, and values and attitudes. It considers grade-level hierarchy by focusing on basic concepts and simple functions for grades 3-4, while emphasizing complex concepts and higher-order functions for grades 5-6. This study contributes to supporting systematic data literacy education in elementary schools by providing a content framework that considers students' cognitive developmental stages and is expected to foster future core competencies through practice-centered education. Further research is needed to verify practical applicability, develop teaching and learning methods, and strengthen connections between school levels
Design and Implementation of a Programming Automatic Assessment System in Jupyter Notebook
Full text linkLearning programming is challenging. So, computer educators have developed various tools to help students. In this paper, we have developed a tool that combines the advantages of a Programming Automatic Assessment (PAA) system and Jupyter Notebook (JN) to support learning programming. The design direction of this system is free to use, easy to set up, and supports interactive computing. The Programming Automatic Assessment in Jupyter Notebook (PAAinJN) is available free of charge using the assessment module released on Git and the personal JN. The initialization is completed by executing in a code cell with two lines of code that downloads and executes the assessment module. In an interactive computing environment, presenting problems, writing code to be evaluated, and evaluating code can be executed in the code cells, and the problems and the results of the assessment are presented in the code cell outputs. The performance was verified by the examples presented in a high school informatics textbook using the programming automatic assessment system as teaching learning material. In addition, we propose a way to develop teaching-learning materials using PAAinJN in consideration of teachers and students and a way of distributing and collecting teaching-learning materials using the free Learning Management System. PAAinJN is expected to help students learn programming by eliminating assessment and feedback delays through PAA while learning to program in an interactive computing environment
An Approach to the Utilization of Design Thinking in Artificial Intelligence Education
Full text linkAs artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future
