Masters Students' Experiences of Learning to Program: An Empirical Model

The investigation reported here examined how Masters students experience learning to program. The phenomenographic research approach adopted permitted the analysis of 1) how students go about learning to program, that is the ‘Act’ of learning to program, and 2) what students understand by ‘programming’, that is the ‘Object’ of learning to program. Analysis of data from twenty-three participants identified five different experiences of the Act of learning to program and five different experiences of the Object of learning to program. Together the findings comprise an empirical model of the learning to program experience amongst the participating students. We suggest how our findings are significant for programming teachers and offer tools to explore students’ views

The Case for Improving U.S. Computer Science Education

Despite the growing use of computers and software in every facet of our economy, not until recently has computer science education begun to gain traction in American school systems. The current focus on improving science, technology, engineering, and mathematics (STEM) education in the U.S. school system has disregarded differences within STEM fields. Indeed, the most important STEM field for a modern economy is not only one that is not represented by its own initial in "STEM" but also the field with the fewest number of high school students taking its classes and by far has the most room for improvement—computer science

Edsger Wybe Dijkstra (1930 -- 2002): A Portrait of a Genius

We discuss the scientific contributions of Edsger Wybe Dijkstra, his opinions and his legacy.Comment: 10 pages. To appear in Formal Aspects of Computin

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