A Mathematical Analysis of Student-Generated Sorting Algorithms

Sorting is a process we encounter very often in everyday life. Additionally it is a fundamental operation in computer science. Having been one of the first intensely studied problems in computer science, many different sorting algorithms have been developed and analyzed. Although algorithms are often taught as part of the computer science curriculum in the context of a programming language, the study of algorithms and algorithmic thinking, including the design, construction and analysis of algorithms, has pedagogical value in mathematics education. This paper will provide an introduction to computational complexity and efficiency, without the use of a programming language. It will also describe how these concepts can be incorporated into the existing high school or undergraduate mathematics curriculum through a mathematical analysis of student-generated sorting algorithms

Embedding Data Analysis into the Undergraduate Actuarial Science Curriculum

The recent initiative by the Actuaries Institute to incorporate a data science/analytics unit at the Honours or Masters level, as well as the changes to the undergraduate curriculum that will start in 2020, means that actuarial science educators need to consider embedding data analysis and analytics throughout the undergraduate curriculum. At Curtin, we have been trialling ways of doing just that in a coherent fashion, from first-year to third-year units, so that students see data analysis as an integral part of becoming a practicing actuary. We were motivated by: 1. The current funding reality, which dictates that units in one degree course serve as units in other courses. At Curtin, for example, we have introduced two new majors: Data Science, and Applied Statistics, both of which have data analytic and computational components, and so many units common to these courses have to do double- and even triple-duty; 2. The fact that many of our actuarial students are finding work as data analysts, and could become even more competitive in the marketplace were they to learn additional skills in computing and data analysis; and 3. The need to modernize the actuarial science curriculum, even before the changes that will take place in 2020. In this talk, I will outline some of the ways in which we have modified tuition and assessment patterns in several units to incorporate computing, data analysis, notions of reproducible analyses, computer-based assessments, and project work. In addition, I will point out what has worked well, as well as some of the barriers we have faced in integrating components of data analytic and computational thinking and practice throughout the entire actuarial science curriculum

Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors.

Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at https://github.com/TheJacksonLaboratory/JAXBD2K-ShortCourse

Teaching movement science with full-body motion capture in an undergraduate liberal arts psychology class

Movement science is a field that is quickly growing in its scope, leaning heavily on psychological expertise for research design with human participants but requiring computational and engineering ability. Undergraduate psychology curricula are in a unique position to train some of its future scholars. This report reviews an attempt to pilot a class on motion capture for undergraduate psychology students. Recent developments in motion-capture technology have opened up the opportunity for giving hands-on experience with high-quality motion capture for students at liberal-arts colleges with leaner research budgets. Post-course responses to the Research on Integrated Science Curriculum (RISC) survey demonstrated that our students made significantly large gains in their ability to organise an empirical approach to study a complex problem with no clear solution, and to collect and analyse data to produce a coherent insight about that problem. Students may benefit from incorporating motion capture into their undergraduate psychology curriculum

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

Educating the educators: Incorporating bioinformatics into biological science education in Malaysia

Bioinformatics can be defined as a fusion of computational and biological sciences. The urgency to process and analyse the deluge of data created by proteomics and genomics studies has caused bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique demand for specialist trained in both biology and computing. In this review, we described the components that constitute the bioinformatics field and distinctive education criteria that are required to produce individuals with bioinformatics training. This paper will also provide an introduction and overview of bioinformatics in Malaysia. The existing bioinformatics scenario in Malaysia was surveyed to gauge its advancement and to plan for future bioinformatics education strategies. For comparison, we surveyed methods and strategies used in education by other countries so that lessons can be learnt to further improve the implementation of bioinformatics in Malaysia. It is believed that accurate and sufficient steerage from the academia and industry will enable Malaysia to produce quality bioinformaticians in the future

On the Prevalence and Nature of Computational Instruction in Undergraduate Physics Programs across the United States

A national survey of physics faculty was conducted to investigate the prevalence and nature of computational instruction in physics courses across the United States. 1246 faculty from 357 unique institutions responded to the survey. The results suggest that more faculty have some form of computational teaching experience than a decade ago, but it appears that this experience does not necessarily translate to computational instruction in undergraduate students' formal course work. Further, we find that formal programs in computational physics are absent from most departments. A majority of faculty do report using computation on homework and in projects, but few report using computation with interactive engagement methods in the classroom or on exams. Specific factors that underlie these results are the subject of future work, but we do find that there is a variation on the reported experience with computation and the highest degree that students can earn at the surveyed institutions.Comment: 8 pages, 6 figure

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space