Learning for the Future: Changing the Culture of Math and Science Education to Ensure a Competitive Workforce

This report argues that improving the math and science skills of our nation's youth is an important step in ensuring and promoting innovation-led economic growth in the coming decades. The report calls for the implementation of a strategic plan that will increase student "demand" for and achievement in mathematics and science

Measuring the effectiveness of computer-based scientific visualisations for conceptual development in Australian chemistry classrooms

Visual modes of representation have always been very important in science and science education. Interactive computer-based animations and simulations offer new visual resources for chemistry education. Many studies have shown that students enjoy learning with visualisations but few have explored how learning outcomes compare when teaching with or without visualisations. This study employs a quasi-experimental crossover research design and quantitative methods to measure the educational effectiveness - defined as level of conceptual development on the part of students - of using computer-based scientific visualisations versus teaching without visualisations in teaching chemistry. In addition to finding that teaching with visualisations offered outcomes that were not significantly different from teaching without visualisations, the study also explored differences in outcomes for male and female students, students with different learning styles (visual, aural, kinesthetic) and students of differing levels of academic ability

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Preparing Undergraduate Students Majoring in Computer Science and Mathematics with Data Science Perspectives and Awareness in the Age of Big Data

AbstractUndergraduate students majoring in Computer Science and Mathematics are entering the workforce not only as programmers and mathematicians but also as data and business intelligent analysts. These job profiles require students to effectively utilize databases and data warehouses technologies, summarize data from external sources including the Internet and provide solutions to complicate, dynamic and ever-changing problems. These areas of hard skills have not been integrated as a major component of undergraduate programs in mathematics and computer science. This paper is aimed at showing how to motivate the significance of mastering data science proficiency as well as depicting examples and resources for lecturers in implementing data science in computer sciences and mathematics curriculum. Two case studies from Computer Science and Informatics Mathematics Programs at Faculty of Science and Technology, Suan Sunandha Rajabhat University in Bangkok, Thailand are presented

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science