scholarly journals Big Data and Data Science: Opportunities and Challenges of iSchools

2017 ◽  
Vol 2 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Il-Yeol Song ◽  
Yongjun Zhu
Keyword(s):  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Computational Thinking ◽  
Building Blocks ◽  
Digital Transformation ◽  
Problem Solving Skills ◽  
Science Courses ◽  
Big Picture ◽  
Eye Opening

AbstractDue to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

scholarly journals Introducing Computational Thinking Practices in Learning Science of Elementary Schools [Research-in-Progress]

10.28945/4327 ◽  
2019 ◽  
Keyword(s):  
Science Education ◽  
Problem Solving ◽  
Computational Thinking ◽  
Learning Science ◽  
Future Research ◽  
Full Potential ◽  
Science Program ◽  
Next Generation ◽  
Science Field ◽  
Problem Solving Skills

Aim/Purpose: Science is becoming a computational endeavor therefore Computational Thinking (CT) is gradually being accepted as a required skill for the 21st century science student. Students deserve relevant conceptual learning accessible through practical, constructionist approaches in cross-curricular applications therefore it is required for educators to define, practice and assess practical ways of introducing CT to science education starting from elementary school. Background: Computational Thinking is a set of problem-solving skills evolving from the computer science field. This work-in-progress research assesses the CT skills, along with science concepts, of students participating in a science program in school. The program pertains learning science by modeling and simulating real world phenomenon using an agent-based modeling practice. Methodology: This is an intervention research of a science program. It takes place as part of structured learning activities of 4th and 5th grade classes which are teacher-guided and are conducted in school. Both qualitative and quantitative evaluations are parts of the mixed methods research methodology using a variety of evaluation technique, including pretests and posttests, surveys, artifact-based interviews, in class observations and project evaluations. Contribution: CT is an emerging skill in learning science. It is requiring school systems to give increased attention for promoting students with the opportunity to engage in CT activities alongside with ways to promote a deeper understanding of science. Currently there is a lack of practical ways to do so and lack of methods to assess the results therefore it is an educational challenge. This paper presents a response to this challenge by proposing a practical program for school science courses and an assessment method. Findings: This is a research in progress which finding are based on a pilot study. The researches believe that findings may indicate improved degree of students' science understanding and problem-solving skills. Recommendations for Practitioners: Formulating computer simulations by students can have great potential on learning science with embedded CT skills. This approach could enable learners to see and interact with visualized representations of natural phenomena they create. Although most teachers do not learn about CT in their initial education, it is of paramount importance that such programs, as the one described in this research, will assist teachers with the opportunity to introduce CT into science studies. Recommendation for Researchers: Scientific simulation design in primary school is at its dawn. Future research investment and investigation should focus on assessment of aspects of the full Computational Thinking for Science taxonomy. In addition, to help teachers assess CT skills, new tools and criteria are required. Impact on Society: STEM related professions are lacking the man power required therefore the full potential of the economy of developed countries is not fulfilled. Having students acquire computational thinking skills through formal education may prepare the next generation of world class scientists and attract larger populations to these fields. Future Research: The inclusion of computational thinking as a core scientific practice in the Next Generation Science Standards is an important milestone, but there is still much work to do toward addressing the challenge of CT-Science education to grow a generation of technologically and scientifically savvy individuals. New comprehensive approaches are needed to cope with the complexity of cognitive processes related to CT.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals University Students' Attitude and Anxiety Towards Computer Science Education through Computational Thinking

2021 ◽  
Vol 12 (6) ◽  
pp. 755-760
Author(s):  
Youngseok Lee Et.al
Keyword(s):  
Science Education ◽  
Problem Solving ◽  
Computer Science ◽  
Learning Effect ◽  
Computer Science Education ◽  
Computational Thinking ◽  
Average Score ◽  
Problem Solving Skills ◽  
Programming Education ◽  
Learning Technique

Background/Objectives: In the 21st century, communication and collaboration between people is an important element of talent. As artificial intelligence (AI), the cutting edge of computer science, develops, AI and collaboration will become important in the near future. Methods/Statistical analysis: To achieve this, it is necessary to understand how artificial AI based on computer science works, and how problem-based programming education is effective in computer science education. In this study, 177 college students who received programming education focused on problem-solving learning were identified with computational thinking (CT) at the beginning of the semester, and their satisfaction and post-education satisfaction survey showed that their attitudes and interests influenced their education. Findings: To pretest the learners, they were diagnosed using a measurement sheet. The learners’ current knowledge statuses were checked, and the correlation between the evaluation results, based on what was taught according to the problem-solving learning technique, was analyzed according to the proposed method. The analysis of the group average score of the learners showed that the learning effect was significant. The results of the measures of the students’ CT at the beginning of the semester were correlated with problem-solving learning, teaching method, lecture satisfaction, and other environmental factors. The ability to solve a variety of problems using CT will become increasingly important, so if students seek to improve their satisfaction with problem-solving learning techniques for computer science education, it will be possible for universities to develop convergence talent more efficiently. Improvements/Applications: if you pursue a problem-solving learning technique and a way to improve students’ satisfaction, it will help students improve their problem-solving skills. If the method of deriving and improving computational thinking ability in this paper is applied to computer education, it will induce student interest, thereby increasing the learning effect.

scholarly journals Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

2016 ◽  
Vol 20 (1) ◽  
pp. 13-28 ◽  
Author(s):  
David H. Olsen ◽  
Pamela A. Dupin-Bryant
Keyword(s):  
Big Data ◽  
Popular Culture ◽  
Learning Outcomes ◽  
Data Science ◽  
Data Driven ◽  
Job Market ◽  
Data Cleansing ◽  
Problem Solving Skills ◽  
5E Instructional Model ◽  
Data Market

Big data and data science have experienced unprecedented growth in recent years.  The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial.  Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses.

scholarly journals Development of a Pandemic Awareness STEM Outreach Curriculum: Utilizing a Computational Thinking Taxonomy Framework

2021 ◽  
Vol 11 (3) ◽  
pp. 109
Author(s):  
Pamela O. Gilchrist ◽  
Alonzo B. Alexander ◽  
Adrian J. Green ◽  
Frieda E. Sanders ◽  
Ashley Q. Hooker ◽  
...  
Keyword(s):  
Public Health ◽  
Problem Solving ◽  
Informal Education ◽  
Teaching And Learning ◽  
Data Science ◽  
Computational Thinking ◽  
Outreach Program ◽  
Learning Approaches ◽  
Health Crisis ◽  
Problem Solving Skills

Computational thinking is an essential skill in the modern global workforce. The current public health crisis has highlighted the need for students and educators to have a deeper understanding of epidemiology. While existing STEM curricula has addressed these topics in the past, current events present an opportunity for new curricula that can be designed to present epidemiology, the science of public health, as a modern topic for students that embeds the problem-solving and mathematics skills of computational thinking practices authentically. Using the Computational Thinking Taxonomy within the informal education setting of a STEM outreach program, a curriculum was developed to introduce middle school students to epidemiological concepts while developing their problem-solving skills, a subset of their computational thinking and mathematical thinking practices, in a contextually rich environment. The informal education setting at a Research I Institution provides avenues to connect diverse learners to visually engaging computational thinking and data science curricula to understand emerging teaching and learning approaches. This paper documents the theory and design approach used by researchers and practitioners to create a Pandemic Awareness STEM Curriculum and future implications for teaching and learning computational thinking practices through engaging with data science.

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