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

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

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

Practice report: six studies of spatial skills training in introductory computer science

We have been training spatial skills for Computing Science students over several years with positive results, both in terms of the students’ spatial skills and their CS outcomes. The delivery and structure of the training has been modified over time and carried out at several institutions, resulting in variations across each intervention. This article describes six distinct case studies of training deliveries, highlighting the main challenges faced and some important takeaways. Our goal is to provide useful guidance based on our varied experience for any practitioner considering the adoption of spatial skills training for their students

Spatial Skills and Demographic Factors in CS1

Motivation Prior studies have established that training spatial skills may improve outcomes in computing courses. Very few of these studies have, however, explored the impact of spatial skills training on women or examined its relationship with other factors commonly explored in the context of academic performance, such as socioeconomic background and self-efficacy. Objectives In this study, we report on a spatial skills intervention deployed in a computer programming course (CS1) in the first year of a post-secondary program. We explore the relationship between various demographic factors, course performance, and spatial skills ability at both the beginning and end of the term. Methods Data was collected using a combination of demographic surveys, existing self-efficacy and CS1 content instruments, and the Revised PVST:R spatial skills assessment. Spatial skills were evaluated both at the beginning of the term and at the end, after spatial skills training was provided. Results While little evidence was found to link spatial skills to socioeconomic status or self-efficacy, both gender identity and previous experience in computing were found to be correlated to spatial skills ability at the start of the course. Women initially recorded lower spatial skills ability, but after training, the distribution of spatial skills scores for women approached that of men. Discussion These findings suggest that, if offered early enough, spatial skills training may be able to remedy some differences in background that impact performance in computing courses

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

An Exploration of Traditional and Data Driven Predictors of Programming Performance

This thesis investigates factors that can be used to predict the success or failure of students taking an introductory programming course. Four studies were performed to explore how aspects of the teaching context, static factors based upon traditional learning theories, and data-driven metrics derived from aspects of programming behaviour were related to programming performance. In the first study, a systematic review into the worldwide outcomes of programming courses revealed an average pass rate of 67.7\%. This was found to have not significantly changed over time, or to have differed based upon aspects of the teaching context, such as the programming language taught to students. The second study showed that many of the factors based upon traditional learning theories, such as learning styles, are context dependent, and fail to consistently predict programming performance when they are applied across different teaching contexts. The third study explored data-driven metrics derived from the programming behaviour of students. Analysing data logged from students using the BlueJ IDE, 10 new data-driven metrics were identified and validated on three independently gathered datasets. Weaker students were found to make a greater percentage of successive errors, and spend a greater percentage of their lab time resolving errors than stronger students. The Robust Relative algorithm was developed to hybridize four of the strongest data-driven metrics into a performance predictor. The novel relative scoring of students based upon how their resolve times for different types of errors compared to the resolve times of their peers, resulted in a predictor which could explain a large proportion of the variance in the performance of three independent cohorts, $R^2$ = 42.19\%, 43.65\% and 44.17\% - almost double the variance which could be explained by Jadud's Error Quotient metric. The fourth study situated the findings of this thesis within the wider literature, by applying meta-analysis techniques to statistically synthesise fifty years of conflicting research, such that the most important factors for learning programming could be identified. 482 results describing the effects of 116 factors on programming performance were synthesised and consolidated to form a six class theoretical framework. The results showed that the strongest predictors identified over the past fifty years are data-driven metrics based upon programming behaviour. Several of the traditional predictors were also found to be influential, suggesting that both a certain level of scientific maturity and self-concept are necessary for programming. Two thirds of the weakest predictors were based upon demographic and psychological factors, suggesting that age, gender, self-perceived abilities, learning styles, and personality traits have no relevance for programming performance. This thesis argues that factors based upon traditional learning theories struggle to consistently predict programming performance across different teaching contexts because they were not intended to be applied for this purpose. In contrast, the main advantage of using data-driven approaches to derive metrics based upon students' programming processes, is that these metrics are directly based upon the programming behaviours of students, and therefore can encapsulate such changes in their programming knowledge over time. Researchers should continue to explore data-driven predictors in the future

ne-Course for Learning Programming

Difficulties in learning programming are a constant concern in engineering courses. In many research studies involving the learning programming must of the solutions presented, from the beginning of the first programming languages, was to apply different type of problems analysis. Literature relating to the understanding of nature of learning programming skills has been focused explicitly on the teaching methodology and few of them focus on abilities, characteristics and knowledge acquired over the life cycle of learning programming in each student. Most of the students enrolled in engineering courses, where programming is a crucial competence, never had the opportunity to develop skills of computational thinking. In this paper, we focus our work on the learning programming developing and applying a set of exercises where students with more difficulties can express and develop their skills in computational thinking. In order to understand some programming students difficulties we have create a set of exercises, and apply it to a pre-programming course, that allows teachers to understand how students analyse and comprehend aspects such as visualization, spatial interpretation and physical manipulation. This paper also reports on results obtained from a class experiment where Memory Transfer Language was used by students to learn programming. All the exercises must be resolved without any type of technology, designed as a ne-course (no electronic course) for learning programming

ne-Course for Learning Programming

Difficulties in learning programming are a constant concern in engineering courses. In many research studies involving the learning programming must of the solutions presented, from the beginning of the first programming languages, was to apply different type of problems analysis. Literature relating to the understanding of nature of learning programming skills has been focused explicitly on the teaching methodology and few of them focus on abilities, characteristics and knowledge acquired over the life cycle of learning programming in each student. Most of the students enrolled in engineering courses, where programming is a crucial competence, never had the opportunity to develop skills of computational thinking. In this paper, we focus our work on the learning programming developing and applying a set of exercises where students with more difficulties can express and develop their skills in computational thinking. In order to understand some programming students difficulties we have create a set of exercises, and apply it to a pre-programming course, that allows teachers to understand how students analyse and comprehend aspects such as visualization, spatial interpretation and physical manipulation. This paper also reports on results obtained from a class experiment where Memory Transfer Language was used by students to learn programming. All the exercises must be resolved without any type of technology, designed as a ne-course (no electronic course) for learning programming