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

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

Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement

Learners’ spatial skill is a reliable and significant predictor of achievement in STEM, including computing, education. Spatial skill is also malleable, meaning it can be improved through training. Most cognitive skill training improves performance on only a narrow set of similar tasks, but researchers have found ample evidence that spatial training can broadly improve STEM achievement. We do not yet know the cognitive mechanisms that make spatial skill training broadly transferable when other cognitive training is not, but understanding these mechanisms is important for developing training and instruction that consistently benefits learners, especially those starting with low spatial skill. This paper proposes the spatial encoding strategy (SpES) theory to explain the cognitive mechanisms connecting spatial skill and STEM achievement. To motivate SpES theory, the paper reviews research from STEM education, learning sciences, and psychology. SpES theory provides compelling post hoc explanations for the findings from this literature and aligns with neuroscience models about the functions of brain structures. The paper concludes with a plan for testing the theory’s validity and using it to inform future research and instruction. The paper focuses on implications for computing education, but the transferability of spatial skill to STEM performance makes the proposed theory relevant to many education communities

Relating Spatial Skills and Expression Evaluation

Work connecting spatial skills to computing has used course grades or marks, or general programming tests as the measure of computing ability. In order to map the relationship between spatial skills and computing more precisely, this paper picks out a particular subset of possible programming concepts and skills, that of expression evaluation. The paper describes the development of an expression evaluation test, which aims to identify participants' ability to perform evaluations of expressions across a range of complexity. The results indicate participants' expression evaluation ability was significantly correlated with a spatial skills test (r=0.48), even more so when only considering those with less prior programming experience (r=0.58). Thus, we have determined that spatial skills are of value in expression evaluation exercises, particularly for beginners

A CS1 Spatial Skills Intervention and the Impact on Introductory Programming Abilities

This paper discusses the results of replicating and extending a study performed by Cooper et al. examining the relationship between students’ spatial skills and their success in learning to program. Whereas Cooper et al. worked with high school students participat- ing in a summer program, we worked with college students taking an introductory computing course. Like Cooper et al.’s study, we saw a correlation between a student’s spatial skills and their success in learning computing. More significantly, we saw that after apply- ing an intervention to teach spatial skills, students demonstrated improved performance both on a standard spatial skills assessment as well as on a CS content instrument. We also saw a correlation between students’ enjoyment in computing and improved perfor- mance both on a standard spatial skills assessment and on a CS content instrument, a result not observed by Cooper et al

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

Investigating the Relationship Between Spatial Skills and Computer Science

The relationship between spatial skills training and computer science learning is unclear. Reported experiments provide tantalising, though not convincing, evidence that training a programming student's spatial skills may accelerate the development of their programming skills. Given the well-documented challenge of learning to program, such acceleration would be welcomed. Despite the experimental results, no attempt has been made to develop a model of how a linkage between spatial skills and computer science ability might operate, hampering the development of a sound research programme to investigate the issue further. This paper surveys the literature on spatial skills and investigates the various underlying cognitive skills involved. It poses a theoretical model for the relationship between computer science ability and spatial skills, exploring ways in which the cognitive processes involved in each overlap, and hence may influence one another. An experiment shows that spatial skills typically increase as the level of academic achievement in computer science increases. Overall, this work provides a substantial foundation for, and encouragement to develop, a major research programme investigating precisely how spatial skills training influences computer science learning, and hence whether computer science education could be significantly improved

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