The Effect of a Spatial Skills Training Course in Introductory Computing

Spatial skills have been associated with STEM success for decades. Research has shown that training spatial skills can have a positive impact on outcomes in STEM domains such as engineering, mathematics and physics; however -- despite some promising leads -- evidence for the same relationship with computing is limited. This research describes a spatial skills intervention delivered to around 60 students in introductory computing courses who tested with relatively low spatial skills, mirroring a well established intervention developed and used by Sorby in engineering for over 20 years. This study has shown correlation between spatial skills and computing assessment marks which was observed both before and after training took place, suggesting that as the students' spatial skills are improved via training, so too is their computing assessment. Students who took part in the intervention also showed a significant increase in class rankings over their peers. The authors consider this to be a good indication that spatial skills training for low spatial skills scorers starting a computing degree is of value

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material

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

Early Developmental Activities and Computing Proficiency

As countries adopt computing education for all pupils from primary school upwards, there are challenging indicators: significant proportions of students who choose to study computing at universities fail the introductory courses, and the evidence for links between formal education outcomes and success in CS is limited. Yet, as we know, some students succeed without prior computing experience. Why is this? <br/><br/> Some argue for an innate ability, some for motivation, some for the discrepancies between the expectations of instructors and students, and some – simply – for how programming is being taught. All agree that becoming proficient in computing is not easy. Our research takes a novel view on the problem and argues that some of that success is influenced by early childhood experiences outside formal education. <br/><br/> In this study, we analyzed over 1300 responses to a multi-institutional and multi-national survey that we developed. The survey captures enjoyment of early developmental activities such as childhood toys, games and pastimes between the ages 0 — 8 as well as later life experiences with computing. We identify unifying features of the computing experiences in later life, and attempt to link these computing experiences to the childhood activities. <br/><br/> The analysis indicates that computing proficiency should be seen from multiple viewpoints, including both skill-level and confidence. It shows that particular early childhood experiences are linked to parts of computing proficiency, namely those related to confidence with problem solving using computing technology. These are essential building blocks for more complex use. We recognize issues in the experimental design that may prevent our data showing a link between early activities and more complex computing skills, and suggest adjustments. Ultimately, it is hoped that this line of research will feed in to early years and primary education, and thereby improve computing education for all

Adoption of innovative e-learning support for teaching: A multiple case study at the University of Waikato

In response to recent social, economic, and pedagogical challenges to tertiary-level teaching and learning, universities are increasingly investigating and adopting elearning as a way to engage and motivate students. This paper reports on the first year of a two-year (2009-2010) qualitative multiple case study research project in New Zealand. Using perspectives from activity theory and the scholarship of teaching, the research has the overall goal of documenting, developing, and disseminating effective and innovative practice in which e-learning plays an important role in tertiary teaching. A “snapshot” of each of the four 2009 cases and focused findings within and across cases are provided. This is followed by an overall discussion of the context, “within” and “across” case themes, and implications of the research

Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice

Two complementary processes may be distinguished in learning a complex cognitive skill such as computer programming. First, automation offers task-specific procedures that may directly control programming behavior, second, schema acquisition offers cognitive structures that provide analogies in new problem situations. The goal of this paper is to explore what the nature of these processes can teach us for a more effective design of practice. The authors argue that conventional training strategies in elementary programming provide little guidance to the learner and offer little opportunities for mindful abstraction, which results in suboptimal automation and schema acquisition. Practice is considered to be most beneficial to learning outcomes and transfer under strict conditions, in particular, a heavy emphasis on the use of worked examples during practice and the assignment of programming tasks that demand mindful abstraction from these examples

Instructional strategies and tactics for the design of introductory computer programming courses in high school

This article offers an examination of instructional strategies and tactics for the design of introductory computer programming courses in high school. We distinguish the Expert, Spiral and Reading approach as groups of instructional strategies that mainly differ in their general design plan to control students' processing load. In order, they emphasize topdown program design, incremental learning, and program modification and amplification. In contrast, tactics are specific design plans that prescribe methods to reach desired learning outcomes under given circumstances. Based on ACT* (Anderson, 1983) and relevant research, we distinguish between declarative and procedural instruction and present six tactics which can be used both to design courses and to evaluate strategies. Three tactics for declarative instruction involve concrete computer models, programming plans and design diagrams; three tactics for procedural instruction involve worked-out examples, practice of basic cognitive skills and task variation. In our evaluation of groups of instructional strategies, the Reading approach has been found to be superior to the Expert and Spiral approaches

Investigating Spatial Skills in Computing Education

There is an intriguing connection between spatial skills and CS: those with better spatial skills tend to do better in many CS related tasks. Since spatial skills are malleable, it is tempting to simply introduce spatial skills training courses to students who are struggling and expect positive outcomes. While improved outcomes are being observed, it would be preemptive to introduce such schemes widely without better understanding the relationship. We do not know why spatial skills are important in CS, so while one might take the gains observed at face value, we stand to lose valuable insights into not only the abstract cognition involved in spatial skills which appears to be of value across STEM, but also reflective and nuanced understanding of how people engage with CS education

Usability Engineering and PPGIS - Towards a Learning-improving Cycle

July 21 - 2