Earticle

본 연구는 계산과학분야의 고성능컴퓨팅 교육을 위한 지식요소들을 제안하고자 한다. 이를 위해 고성능컴퓨팅 전문가들을 대상으로 1차 설문을 통해 내용타당도와 신뢰도를 조사하여 20개의 후보지식요소들을 도출하였다. 고성능컴퓨팅 사용자들을 대상으로 2차 설문을 통해 후보지식요소들에 대해 t-test, Borich 요구도, The Locus for Focus모델을 적용하여 고성능컴퓨팅 교육을 위한 10개의 지식요소들을 도출하였다. 그 결과 고성능컴퓨팅 교육을 위한 ‘기본 병렬성’, ‘병렬성’, ‘병렬통신 및 조정’, ‘병렬분할’, ‘병렬 알고리즘 분석 및 프로그래밍’, ‘모델링 및 시뮬레이션 소개’ 6개의 1순위 지식요소들과 ‘기본 프로그래밍 개념’, ‘기본 자료구조’, ‘메모리 관리’, ‘알고리 즘 및 설계’ 4개의 2순위 지식요소들을 도출하였다.

The purpose of this study is to suggest the knowledge elements for HPC education in computational science. For this purpose, the survey for HPC experts was conducted to verify the content validity and reliability, and the 20 candidate knowledge elements was extracted. And the second survey for HPC users was conducted to apply the t test, Borich requirement, and The Locus for Focus model. And 10 knowledge elements for HPC education were derived. As a result, the first group was 'Parallelism Fundamentals', 'Parallelism', 'Parallel communication and coordination', 'Parallel Decomposition', 'Parallel Algorithms, Analysis, and Programming’ and 'Introduction to Modeling and Simulation', 'Fundamental Programming Concepts', 'Fundamental Data Structures', ‘Memory Management’, 'Algorithms and Design' were second group for HPC education.

계산과학분야에서 고성능컴퓨팅(HPC)을 활용하기 위해서는 프로그래밍, 알고리즘, 자료구조 등 컴퓨터과학의 지 식들과 기술들을 배워야 한다. 본 논문에서는 계산과학분야의 IT교육현황 조사와 설문조사를 통해 고성능컴퓨팅 교육을 개선시키기 위한 정책 방향을 제안하는데 있다. 이를 위해 국내 대학의 물리학, 화학, 생명과학, 지구과학분야의 전공과목 중에서 IT관련 과목 현황과 사용자들의 국내 고성능컴퓨팅 교육에 대한 인식을 조사하였다. 그 결과 계산과학분야의 IT과 목비율은 응용 전공과목에 비해 매우 낮았다. 대학의 교육 요구도는 높게 나왔지만, 대학의 교육 제공 수준은 제일 낮게 나왔다. 또한 대부분의 사용자들은 독학으로 필요한 지식과 기술들을 습득한 것으로 조사되었다. 즉 대학의 역할이 가장 시급하고 중요하며 전문기관과 온라인교육의 역할도 중요하다고 확인하였다.

In order to utilize HPC in Computational science, It is necessary to learn the knowledge and skills of computer science such as programming, algorithms and data structure. In this paper, we investigate IT education status in Computational science and propose policy directions to improve the HPC education through user survey. To do this, we surveyed the current state of IT subjects among major subjects in physics, chemistry, life sciences, and earth science in domestic universities and surveyed the users' Recognition of HPC education. As a result, the ratio of IT subjects in Computational science was very lower than the ratio of major domain subjects. Despite the high educational needs of universities, the educational level of universities was the lowest. Most users have learned the necessary knowledge and skills through self-study. We recognized the role of the university is the most urgent and important, and the role of professional institutions and online education is also important.

본 연구는 2021년부터 2023년 6월까지 최근 3년간 국내에서 진행된 28편의 컴퓨팅 사고력 기반 데이터 과학 교육 연구의 동향을 분석하여 향후 데이터 과학 교육의 방향성과 시사점을 도출하고자 하였다. 분석 결과를 통 해 컴퓨팅 사고력 기반 데이터 과학 연구가 해마다 증가하는 양상을 보이는 것을 알 수 있었다. 또한, 초등학생 대상의 연구가 가장 많이 이루어지고 있으며 중·고등학생, 대학생 이상의 성인을 대상으로 한 연구의 수는 상대 적으로 적게 나타났다. 연구 영역을 살펴보면 교수학습모형 개발 연구 4편을 제외하고는 모두 교육 프로그램 개 발 및 적용을 다루고 있음을 알 수 있다. 교육 현장에는 컴퓨팅 사고력 기반의 데이터 과학 교육을 어떻게 적 용하고 어떤 효과가 나타나는지에 대한 실제적 사례에 대한 관심과 수요가 많다. 그러나 대부분의 연구들은 효 과성 검증까지 실시하였으나, 일부 효과성 검증이 미포함된 연구들이 있어 후속 연구가 필요한 상황이다.

This study aims to derive the direction and implications of data science education in the future by analyzing the trends of 28 computational thinking-based data science education studies conducted in Korea for the past three years from 2021 to June 2023. Through the analysis results, it was found that data science research based on computational thinking is increasing year by year. In addition, most of the studies targeting elementary school students are conducted, and the number of studies targeting adults over middle and high school students and university students is relatively small. Looking at the research areas, it can be seen that all of them deal with the development and application of educational programs except for the four teaching-learning model development studies. In the field of education, there is a lot of interest and demand for practical examples of how to apply computational thinking-based data science education and what effects it has. However, most of the studies have even conducted effectiveness verification, but there are studies that do not include some effectiveness verification, so follow-up studies are needed.

2022 개정 교육 과정에서 초등 정보 교육의 시수를 연간 34차시 이상 운영하도록 하는 등 정보 교육에 대한 관심과 요구가 커지고 있다. 그러나 현재 초등교육에서 정보 교육은 블록 코딩 위주의 프로그래밍이 대부분을 차지 하고 있고, 인공지능의 핵심인 데이터에 집중한 수업은 찾아보기 어려운 상황이다. 이에 본 연구에서는 학 생들의 컴퓨팅 사고력을 함양시킬 수 있는 데이터 과학과 국어, 도덕과와 융합한 교수·학습 방법을 제안하고, 수 원 S초등학교 학생 25명을 대상으로 수업을 실시했다. 수업을 적용한 결과 모든 학생들이 데이터 과학과 도덕, 국어의 학습 목표를 달성한 것으로 나타났다.

Interest and demand for information education are growing, with the number of hours of elementary information education operating more than 34 times a year in the 2022 revised curriculum. However, information education is currently dominated by block coding-oriented programming in elementary education, and it is difficult to find classes that focus on data, which is the core of artificial intelligence. Therefore, this study proposed data science, Korean language, and moral studies that can foster students' computational thinking, and conducted classes for 25 students at Suwon S Elementary School. As a result of applying the class, it was found that all students achieved the learning goals of data science, morality, and Korean.

사회 각종 분야에서 데이터 과학이 활용되는 비율이 높아지고 있음에도 불구하고 데이터 과학 교육 프로그램 에 대한 연구는 비교적 미비한 실정이다. 본 연구에서는 초등학생을 대상으로하는 데이터 과학 교육 프로그램을 개발하여 그 효과를 검증하였다. 피지컬 컴퓨팅 도구 중에 하나인 마이크로비트를 활용하여 데이터를 수집하는 프로그램을 제작해 보고, 수집된 데이터를 분석하여 결과를 도출하는 데이터 과학의 단계를 수행하는 교육 프로 그램을 개발하였다. 00대학교 정보영재과정을 수강하고 있는 초등학생 6학년 10명을 대상으로 연구를 진행하였 으며, 효과를 검증하기 위하여 컴퓨팅 사고력 사전·사후 검사를 실시하였다. 그 결과 본 연구를 통해 개발된 데 이터 과학 교육 프로그램이 초등학생의 컴퓨팅 사고력을 향상시키는 데 유의미한 효과가 있음을 알 수 있었다.

Despite the increasing rate of use of data science in various fields of society, research on data science education programs is relatively inadequate. In this study, a data science education program for elementary school students was developed and its effectiveness was verified. We created a program that collects data using microbit, one of the physical computing tools, and developed an education program that performs the data science stage of analyzing the collected data to derive results. A study was conducted on 10 students enrolled in the Information Gifted Program at 00 University, and pre- and post-tests of computing thinking skills were conducted to verify the effectiveness. As a result, it was found that the data science education program developed through this study has a significant effect on improving the computational thinking of elementary school students.

본 연구는 초등학생의 컴퓨팅 사고력 향상을 위한 교육 방법으로 스프레드시트를 활용한 데이터 과학 교육 프로그램을 개발하여 적용한 후 그 효과를 검증하였다. 교육 프로그램은 Rossett의 요구 분석 모형을 적용하여 초등학생 205명과 컴퓨터교육 전공 현직 초등교사 20명을 대상으로 실시한 사전 요구분석 결과를 바탕으로, 교 수설계의 대표 모형인 ADDIE 모형의 절차에 따라 개발하였다. 개발한 교육 프로그램의 효과를 검증하기 위해 ◯◯대학교에서 실시한 교육기부 프로그램의 지원자 표집에 의한 지원자 표본 20명의 학생을 대상으로 총 6일 동안 42차시 수업을 진행하였고 사전·사후 검사 결과를 통해 교육적 효과를 분석하였다. 분석 결과, 본 연구에서 개발한 교육 프로그램이 초등학생의 컴퓨팅 사고력 향상에 효과적임을 알 수 있었다.

In this study, we developed a data science education program using spreadsheet, applied it after educational method to improve elementary school student 's Computational Thinking, and then verified its effect. Based on the results of preliminary requirement analysis conducted by Rossett's request analysis the educational program was developed based on the procedure of the ADDIE model which is the representative model of the teaching design based on the result of prior requirement analysis of 205 elementary school students and computer teaching major 20 incumbent elementary school teachers, applying Rossett's requirement analysis model. In order to verify the effect of the developed educational program, we are promoting 42 hours of lecture for a total of 6 days for 20 students of applicants who volunteered for volunteer votes of educational donation programs implemented at ◯◯University, We analyzed the educational effect using the results of pre-post test. As a result of the analysis, we learned that the educational program developed in this study is effective for improving elementary school student 's Computational Thinking.

본 연구에서는 고등학교 지구과학 I 고체지구 영역 에서 컴퓨팅 사고력 기반의 교수학습활동을 개발하고, 그 효과와 시사점을 살펴보았다. 이를 위해 총 8차시 에 해당하는 문제해결 중심 언플러그드 활동을 개발하 고, 이를 A 고등학교 학생 65명을 대상으로 적용하였 다. 교수학습활동을 실시한 결과 학생들의 컴퓨팅 사 고력과, 컴퓨팅 사고력에 대한 인식 측면에서 유의미 한 변화를 확인하였다. 활동 이후 학생들의 자기보고 서의 응답과 면담자료를 토대로 지구과학 문제해결과 정에서 컴퓨팅 사고력과 컴퓨팅 사고력에 대한 인식에 영향을 준 요인을 살펴보았다. 또한 컴퓨팅 사고력 기 반의 교수학습설계와 진행의 측면에서 고려해야 하는 요소에 대한 시사점을 도출하였다. 연구 결과를 토대 로 지구과학을 포함한 다양한 교과목을 통한 컴퓨팅 사고력 개발을 위한 교수학습활동의 필요성과 운영방 안을 제안하였다.

In this study, teaching-learning activities were developed based on computational thinking (CT) in high school Earth Science I Solid Earth domain. And their effects and implications were examined. To this end, 8 sessions unplugged activities based on problem solving were developed, and applied it to 65 high school students. As a results, significant changes have been were confirmed in both student’s perception about CT and CT. After that, based on student’s responses to self-reports and interview, the affecting factors for perception about CT and CT in the earth science problem solving process were investigated. In addition, implication that should be considered in terms of design and progress of teaching-learning based on CT were derived. Based on these results, the necessity and operation of teaching-learning activities was suggested for developing CT through various subjects including earth science.

본 연구의 선행 연구에서는 1∼3차에 걸쳐 초등학생의 컴퓨팅 사고력 신장을 위한 퍼즐 기반 컴퓨터과학 교 육 프로그램을 개발하여 현장에 투입하며 교육 효과를 CT창의성과 CT인지력으로 나눠 검증하고 교육 프로그램 을 개선해왔다. 본 연구에서는 이러한 선행 연구 결과를 바탕으로 연령과 CT사고력 하위 요소를 매개 변수로 사용하여 계층적 베이지안 추론 모델링을 실시하였다. 그리고 그 결과를 바탕으로 중·고등학교에서 퍼즐 기반 컴퓨터과학 교육 프로그램의 효과를 예측하고 향후 투입할 중·고등학교의 퍼즐 기반 컴퓨터과학 교육 프로그램 의 주요 개선 항목과 개선 방향을 제안하였다.

The preceding study of this study developed puzzle-based computer science education programs to enhance the computational thinking of elementary school students over 1 to 3 times. The preceding study then applied such programs into the field, categorized the effects of education into CT creativity and CT cognitive ability to improve the education programs. Based on the results of these preceding studies, the hierarchical Bayesian inference modeling was performed using age and CT thinking ability as parameters. From the results, this study predicted the effectiveness of puzzle-based computer science education programs in middle and high schools and proposed major improvement areas and directions for puzzle-based computer science education programs that are to be deployed in the future throughout middle and high schools.

본 연구는 앱인벤터를 활용한 PBL 기반 데이터 사이언스 교육 수업이 초등학생의 컴퓨팅 사고력과 창의성에 미치는 영향을 알아보기 위한 연구이다. Rossett의 요구분석 모형에 의한 사전 요구분석 결과를 바탕으로 ISD모 형인 ADDIE 모형의 절차에 따라 PBL 기반의 데이터 사이언스 교육 수업을 설계하여 초등학생을 대상으로 42 차시의 수업을 실시하였다. 대응표본 t검정 결과, 컴퓨팅 사고력은 컴퓨팅 사고력은 사후 검사에서 통계적으로 유의미하게 향상되었음이 입증되었다. 또한, 대응표본 t검정, Wilcoxon 부호 순위 검정 결과 창의성의 하위 요소 인 ‘독창성’, ‘정교성’, ‘성급한 종결에 대한 저항’과 ‘창의성 평균’, ‘창의성 지수’에서 통계적으로 유의미하게 향상 된 것으로 나타났다. 따라서, 앱인벤터를 활용한 PBL 기반 데이터 사이언스 교육 수업이 초등학생의 컴퓨팅 사 고력과 창의성 향상에 효과가 있음을 확인할 수 있었다.

The purpose of this study is to investigate the effects of Data Science Education classes using PBL-based App Inventor on elementary student Computational Thinking and Creativity. Based on the results of the pre-requisite analysis by Rossett’s demand analysis model, PBL-based Data Science Education class was designed according to the procedure of ADDIE model which is 42 hours of classroom instruction for elementary student. As a result of the Paired t-test, it was proved that the Computational Thinking was statistically significantly improved in the post-test. In addition, as a result of the Paired t-test and Wilcoxon’s signed rank test, it was found that the sub-factors of Creativity were ‘Originality’, ‘Fluency’, ‘Closure’, ‘Average’, and ‘Index’. Therefore, it was confirmed that the PBL-based Data Science Education class using App Inventor is effective in improving Computational Thinking and Creativity of elementary student.

컴퓨팅 사고력은 디지털 사회에서 핵심 인재로 성장하기 위한 기초 학습 능력으로서 강조되고 있다. 국가적으 로 컴퓨팅 교육에 대해 많은 관심과 논의가 이루어지고 있으며, 이에 우리 정부도 컴퓨팅 교육을 교육과정에 포 함시켰다. 이 같은 변화에 따라 예비교사들이 갖추어야 하는 컴퓨터 활용 능력의 수준이 높아지고, 디지털 사회 에 맞추어 컴퓨팅 사고력 지도자로서의 역할이 교사들에게 강조되고 있다. 따라서 예비 교사 양성 교육과정에서 부터의 다양한 프로그래밍 학습을 통해 컴퓨팅 사고력을 이해할 수 있는 교육과정이 필요하다. 본 연구에서는 비전공자 예비교사를 대상으로 한 프로그래밍 교육과정을 설계하고 교육을 진행하였으며, 개발한 설문 도구를 통해 비전공자 예비교사의 컴퓨팅 사고력 함양에 대한 프로그래밍 교육의 효과성을 분석하였다.

In Computational thinking is emphasized as a basic learning ability to grow into a key talent in the digital society. There is much interest and discussion on computing education nationally, and the Korean government has also included education in the curriculum. As a result of these changes, the level of computer literacy that preliminary teachers need to be imporved, and the role of computational thinking as a leader in digital society is being emphasized by teachers. Therefore, it is necessary to have a curriculum that can understand computational thinking through various programming learning from preliminary teacher education curriculum. In this study, we designed and taught programming curriculum for non-majored preliminary teachers. Through the developed questionnarie, we analyzed the affect of programming education on the preliminary teacher's development of computational thinking ability.

데이터 기반 사회를 살아갈 학생들에게 데이터 활용 역량은 미래 사회에서 갖추어야 할 핵심 역량이 되었다. 데 이터 과학의 중요성에 비해 초등교육 맥락에서 이를 어떻게 가르쳐야 하는지에 대한 연구는 아직 부족한 편이다. 이에 본 연구는 구성주의 관점에서 초등학교 교사가 컴퓨팅 사고력 기반 문제 해결 활동 중심의 데이터 과학 수업 을 설계 및 실행할 때 활용할 수 있는 구체적인 교수학습 전략 개발을 목적으로 하였다. 선행연구 분석을 통해 도 출한 교수학습 전략에 대하여 10인의 전문가를 대상으로 총 2회의 델파이 조사를 실시하여 타당도를 확보하였다. 본 연구는 구성주의 관점에서 학습자의 자기 주도적 문제 해결 과정을 지원할 수 있는 교수학습 전략을 개발하였다 는 점, 초등교육 맥락에서 데이터 과학을 가르치는 교사가 단계별로 활용할 수 있는 구체적인 교수학습 전략과 지 침을 마련하였다는 점에서 의의가 있다.

For students who will live in a data-based society, the ability to utilize data has become a core competency that must be equipped in the future society. Despite the importance of data science, research on how to teach it in the context of elementary education is still lacking. Therefore, this study aims to develop specific teaching and learning strategies that elementary school teachers can use when designing and implementing data science education centered on computational thinking-based problem-solving activities from the constructionism. A total of 2 Delphi surveys were conducted targeting 10 experts on the teaching-learning strategies derived through the analysis of preceding studies to ensure validity. This study is significant in the following respects. First, we developed a teaching-learning strategy that can support learners' self-directed problem-solving process from a constructionism. Second, we developed specific teaching-learning strategies and guidelines that teachers who teach data science in the context of elementary education can use step by step.

이 연구에서는 창의교육의 일환으로 컴퓨팅 사고력 을 개발할 수 있는 블록형 코딩프로그램을 활용해 지구과학 교수·학습에 적용하여 컴퓨팅 사고력에 대한 인식과 과학적 태도가 어떻게 변화되는지를 알아보고 자 하였다. 연구 결과를 바탕으로 결론을 정리하면 다 음과 같다. 첫째, 초등학생들에게 적용할 수 있는 블록 형 코딩프로그램을 활용한 지구과학 교육프로그램을 개발하였다. 총 12차시로 구성된 프로그램은 학생 활 동중심의 문제를 제공하여 다양한 사고를 학생들이 유 도할 수 있도록 탐구활동을 설계하였다. 둘째, 블록형 코딩프로그램을 활용한 지구과학 교육프로그램을 통 해 컴퓨터 프로그램 활용에 대한 자신감, 컴퓨터 활용 통합학습, 컴퓨팅 사고력에 대한 자신감 및 컴퓨팅 사 고력을 활용한 문제해결 요인에서 유의미한 결과를 나 타냈다. 셋째, 블록형 코딩프로그램을 활용한 지구과 학 교육프로그램은 호기심, 비판성, 협동성, 끈기, 창의 성 항목에서 유의미한 차이를 나타내었다. 이것은 학 생들이 문제를 접했을 때 의문을 갖고 이를 해결하는 과정에서 효과를 나타낸 것이라고 판단할 수 있다.

In this study, a block-base coding that could develop computing thinking was applied to Earth science teaching and learning to identify how the perception of computational thinking and scientific attitude was changed as part of creativity education. Based on the results of the study, the conclusions are as follows: First, an Earth science education program was developed using a block-based coding for elementary school students. The 12-hour program was designed for inquiry activities to encourage students to engage in various thinking by providing them with activity-oriented problems. Second, the Earth science education program using a block-based coding showed significant results in confidence in the use of a computer program, integrated learning with a computer, computational thinking, and problem-solving factors with computational thinking. Third, the Earth science education program using block-based coding showed significant differences in the categories of curiosity, criticism, cooperation, persistence, and creativity. It could be judged that it was effective for students in the process of questioning and trying to solve the problem themselves.

Traditionally computational scientists have used supercomputers to solve their scientific problems using multi-processors and large-scale shared memory, and scientific communities have also used Grid computing for scientific projects using large-scale computation and data resources. Recently Cloud computing is a emerging infrastructure to be considered for scientific applications and several institutes applied this to their projects. But there is no sure method for computational scientists to select a environment to fit their researches though diverse researches using these infrastructures are conducted in a variety of science domains. In this paper we describe three infrastructures for scientific researches through literature reviews, and we deliberate on what factors are considered to select one infrastructure to solve scientific problems from a scientists’ point of view. And then, analytic hierarchy process is introduced as an approach for choosing a proper infrastructure, and we propose a model to choose a appropriate infrastructure reflected by various aspects of computational science’s properties via this approach. Thus this paper offers a novel viewpoint to scientists when they choose a high performance computing environment for their researches.

High Performance Computer (also called "supercomputer") is a computer which can process the complex and large-scale operation in computational science. In general, High Performance Computer represents the top 500 computers which is based on the computer performance in the world. After the successful commercialization of CDC6600 which has the 9MFLOPS performance in 1964, TFLOPS and PFLOPS High Performance Computer have been developed. High Performance Computer is the public resource which can be developed and provided by government. For the efficient application of High Performance Computer, government has to support the related activities such as research, resource allocation, and professional manpower training. To build a basis of the development of High Performance Computing can improve the quality of life and develop the national economy. For this reason, Korean government preceded the establishment of law for national supercomputing promotion from 2009. As a result of this effort, Korean government enacted the 'National Supercomputing Promotion Act' in 2011. This study provides the domestic and international trend of High Performance Computer, and analyses the major contents of National Supercomputing Promotion Act. This effort will be helpful to propose the strategy in order to provide more efficient service as a momentum of national supercomputing center.

Since ‘teaching coding’ or ‘programming’ classes for computational thinking (CT) education in K-12 are renowned around the world, a pre-service teachers’ readiness for integrating CT into their teaching subjects is important due to the fact that CT is considered to be another ‘R’ from algoRitm for 21st century literacy, in addition to the traditional 3R (Reading, Writhing, and Arithmetic) [2] and CT roles to other disciplines. With this rationale, we designed a guided instruction based CT course for pre- service teachers. We show the effectiveness of the program with respect to the teachers’ attitude toward combining CT into their teaching subjects, and mindset changes of learning computing connected with the career development of the teacher themselves. The research focused on the instructional methodology of teaching programing for non-Computer Science Education (CSE) majors who are not familiar with computer science for alleviating the cognitive load of first exposure to programming course under the CT concepts.

The Standard Model of particle physics was established after discovery of the Higgs boson. However, little is known about dark matter, which has mass and constitutes approximately five times the number of standard model particles in space. The cross-section of dark matter is much smaller than that of the existing Standard Model, and the range of the predicted mass is wide, from a few eV to several PeV. Therefore, massive amounts of astronomical, accelerator, and simulation data are required to study dark matter, and efficient processing of these data is vital. Computational science, which can combine experiments, theory, and simulation, is thus necessary for dark matter research. A computational science and deep learning-based dark matter research platform is suggested for enhanced coverage and sharing of data. Such an approach can efficiently add to our existing knowledge on the mystery of dark matter.

This review deals with computational treatments of subatomic levels of matter based on density functional theory (DFT), and tries to identify several current trends, which are largely consequences of the ever-increasing power of computers, which has substantially extended the performance of conventional DFT beyond its original scope. This review mainly focuses on the conceptual outline, rather than on lines of equations, highlighting several examples of calculations for each topic. It should be noted that these issues are hardly new to leading groups in the field, but certainly are for materials people in general. It should also be noted that the on-going efforts will continue and lead to a larger system size, a longer time scale, a higher accuracy, and a better efficiency of calculation for years to come.

David Chalmers characterizes the central commitments of computational cognitive science in terms of two theses: computational sufficiency, the idea that the right kind of computational structure suffices for the possession of a mind, and computational explanation, the idea that computation provides a general framework for the explanation of cognitive processes and behavior. The computational program has been challenged by Hilary Putnam (1988)and John Searle (1991), who argue that every physical system implements every computation, with the consequence that any computational ‘explanation’of cognition is utterly trivial. What is needed, according to Chalmers, is an account of implementation, which would both answer the Searle/Putnam challenge and provide a foundation for computational cognitive theorizing. In this paper I argue that computational cognitive models typically do not satisfy Chalmers’ notion of implementation, and so his account does not provide a conceptual foundation for computational theorizing as it is actually practiced. I argue further that the ‘in-principle’ possibility of deviant implementations of the Putnam/Searle sort does not undermine that practice ? it does not make computational explanation trivial ? though seeing why it doesn’t requires that we take account of the use to which a computation is put in the exercise of a cognitive capacity.

Science fiction itself has been an overwhelmingly male-dominated genre in both readership and authorship. From a stylistic perspective, this paper aims to explore the following questions. Will the language distance between female science fiction writers and male science fiction writers be smaller than that of non-science fiction writers? In comparison to male writers, what are issues concern female SF writers most and how do they construct their identity in their novels? In this research, the author classifies science fiction (SF) into two categories according to gender of the authorship?female science fiction (f-SF) and male science fiction (m-SF). Since R Stylo can combine variable extraction with statistical analysis in a single process, the author adopts this software to calculate the distance (the degree of distance) between these two types of novels to examine whether their distance is insignificant enough to show that science fiction is gender-neutral in language. The primary statistic in this research shows that there is no significant difference between the two groups of statistics, which means that specific attention (both prejudice and stereotype) should not be assigned to female writers. Meanwhile, it has been found that female writers are eager to engage both women as individuals and in groups relating to city construction and governance.

The purpose of science education is scientific literacy, which is extended in its meaning in the $21^{st}$ century. Students must be equipped with the skills necessary to solve problems from the community beyond obtaining the knowledge from curiosity, which is called 'computational thinking'. In this paper, the authors tried to define computational thinking in science education from the view of scientific literacy in the $21^{st}$ century; (1) computational thinking is an explicit skill shown in the two steps of abstracting the problems and automating solutions, (2) computational thinking consists of concrete components and practices which are observable and measurable, (3) computational thinking is a catalyst for STEAM (Science, Technology, Engineering, Arts, and Mathematics) education, and (4) computational thinking is a cognitive process to be learned. More implication about the necessity of including computational thinking and its emphasis in implementing in science teaching and learning for the envisioned scientific literacy is added.