What best predicts computer proficiency?

Evans, Gerald E.; Simkin, Mark G.

doi:10.1145/68814.68817

Cited by 168 publications

(99 citation statements)

References 18 publications

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“…Considered as such, the findings on its predictiveness are in line with previous research. The importance of computer game playing as a predictor of performance supports the results of Cantwell-Wilson and Shrock (2001) and Evans and Simkin (1989), who similarly found a negative relationship between game playing and programming performance.…”

Section: Analysis Of Predictor Setsupporting

confidence: 80%

“…Cantwell-Wilson and Shrock (2001) found that the number of hours students played computer games was negatively related to performance on an introductory computer science course. Similarly, Evans and Simkin (1989) found the number of hours students spent playing electronic games (both video and computer games were studied) had a negative relationship with performance on an introductory Basic course. They also found that the number of hours a student spent working at a part-time job negatively relates to performance.…”

Section: Review Of Literaturementioning

confidence: 96%

“…Numerous studies, including Leeper and Silver (1982), Honour-Werth (1986), Byrnes and Lyons (2001), Cantwell-Wilson and Shrock (2001), Evans and Simkin (1989), and Bergin and Reilly (2005a), have found that mathematical ability and exposure to mathematics courses are important predictors of performance on introductory computer science modules. Similarly, Byrnes and Lyons (2001) and Leeper and Silver (1982) found that performance in and experience of science subjects is also important.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Similarly, Byrnes and Lyons (2001) and Leeper and Silver (1982) found that performance in and experience of science subjects is also important. Studies by Hagan and Markham (2000), Holden and Weeden (2004), Evans andSimkin (1989), andCantwell-Wilson andShrock (2001) have ascertained that prior programming experience and non-programming computer experience are useful predictors of programming performance.…”

Section: Review Of Literaturementioning

confidence: 99%

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Predicting introductory programming performance: A multi-institutional multivariate study

Bergin

Reilly

2006

Computer Science Education

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A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

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Section: Analysis Of Predictor Setsupporting

confidence: 80%

Section: Review Of Literaturementioning

confidence: 96%

Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

See 2 more Smart Citations

Predicting introductory programming performance: A multi-institutional multivariate study

Bergin

Reilly

2006

Computer Science Education

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“…Other researchers have studied characteristics and traits in personality types for certain roles in software engineering [21], [22], [23], and [24]. System Analysts and Programmers are among the more explored roles in these studies.…”

Section: Introductionmentioning

confidence: 99%

Influence of personality types in software tasks choices

Capretz

Varona

Raza

2015

Computers in Human Behavior

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Abstract-According to psychology, not everybody can excel at all kinds of tasks. Thus, chances of a successful outcome of software development increase if people with particular personality traits are assigned to their preferred tasks in the project. Likewise, software development depends significantly on how software practitioners perform their tasks. This empirical study surveys 100 Cuban software developers, both students and professors of the University of Informatics Sciences in Havana, Cuba. This work aims to find possible patterns that link personality traits to role preferences in a software life cycle. Among the various roles, system analyst, software designer, and programmer are found to be the most preferred among the participants. In contrast, tester and maintainer happen to be the least popular roles among software engineers.Index Terms-Human factors in software engineering, Software life cycle, Human aspects of software development, Software psychology I. INTRODUCTION AND BACKGROUND Software engineering has been one of the most prominent professions over the last 20 years, and it is projected to evolve even further. Software engineering comprises stages in distinct areas, such as analysis, design, programming, testing, and maintenance. Today, specialties within software engineering are as diverse as in any other profession. Additionally, software engineers need to communicate more effectively with users and team members, thus the people dimension of software engineering is as important as technical expertise.Software project managers have always faced the problem of assigning the tasks to the right people within a team in such a fashion that increases the chance of successful project completion [1]. Different ideas have been tried to use diverse ways to maximize performance [2] and make choices in the software engineering process [3]. Those ideas involve: motivation (software engineers tend to perform better if they are motivated to do specific tasks), the environment, and personality type, or a combination of these factors. Motivation and the environment are known to influence task performance. Motivation is generally a powerful element in the performance of task goals, especially in the IT field [4], [5]; however, motivation is often insufficient for influencing performance of goals on its own. Similarly, environmental factors cannot independently generate the performance of tasks. Hence, there are multiple factors involved in the performances of software engineers [6]. This study specifically investigates the role of individual preferences in software projects, while neglecting the elements of motivation and environment, which have been the focus of most scholarly research on this topic. Feldt et al. [7] also state that environmental factors alone cannot improve task performance. Thus this work exclusively investigates the role of individual preferences in software projects, focusing explicitly on how personality types affect preferences for specific software roles.Several studies investigate th...

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