Empirical validation and application of the computing attitudes survey

Dorn, Brian; Tew, Allison Elliott

doi:10.1080/08993408.2015.1014142

Cited by 74 publications

(49 citation statements)

References 28 publications

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“…The first set included relevant questions from [12], as well as AI-oriented questions, and allowed us to establish a baseline for the attitudes and abilities of our admitted students. Relevant materials include [13,14]. The second set of surveys focused on the admitted students pre and post participation in the 2 week summer program.…”

Section: Discussionmentioning

confidence: 99%

“…The two questions measuring interest in AI (Q13 and Q17) were grouped together when tested, giving a total of 10 t-tests. The paired t-test analysis with BenjaminiHochberg procedure found that questions 3,8,9,10,11,12,14, and 15 show a statistically significant (α = .05) positive difference between pre-SAILORS student answers and post-SAILORS. Thus, there exists statistically significant evidence that SAILORS relates to 1) increase in confidence in CS (Q3 and Q14); 2) increase in confidence in AI (Q15); 3) increase in likelihood of pursuing CS (Q9 and Q11); 4) increase in likelihood of pursuing AI (Q10 and Q12); and 5) increase in same-sex peer-to-peer support (Q8).…”

Section: Pre/post Surveymentioning

confidence: 94%

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Toward More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

Vachovsky

Chaturapruek

et al. 2016

Proceedings of the 47th ACM Technical Symposium on Computing Science Education

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The field of computer science suffers from a lack of diversity. The Stanford Artificial Intelligence Laboratory's Outreach Summer (SAILORS), a two-week non-residential free summer program, recruits high school girls to computer science, specifically to Artificial Intelligence (AI). The program was organized by graduate student and professor volunteers. The goals of the pilot program are to increase interest in AI, contextualize technically rigorous AI concepts through societal impact, and address barriers that could discourage 10th grade girls from pursuing computer science. In this paper we describe the curriculum designed to achieve these goals. Survey results show students had a statistically significant increase in technical knowledge, interest in pursuing careers in AI, and confidence in succeeding in AI and computer science. Additionally, survey results show that the majority of the students found new role models, faculty support, and a sense of community in AI and computer science.

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Section: Discussionmentioning

confidence: 99%

Section: Pre/post Surveymentioning

confidence: 94%

Toward More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

Vachovsky

Chaturapruek

et al. 2016

Proceedings of the 47th ACM Technical Symposium on Computing Science Education

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“…Digging deeper, the 29 unique constructs measured in the Affect subcomponent were found across a subset of the 31 evaluation instruments, indicating that instrument authors have developed different instruments that measure a similar construct. For example, six instruments measure the construct Interest in CS: Computing Attitudes Survey [8], BA-SICS Study Student Implementation Questionnaire [26], Computer Science Attitude Survey [17], Robotics Activities Attitudes Scale [6], and Computer Science Attitude and Identity Survey [31].…”

Section: Discussionmentioning

confidence: 99%

A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education

McGill

Decker

McKlin³

et al. 2019

Proceedings of the 50th ACM Technical Symposium on Computer Science Education

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A growing body of evidence indicates that there is a deep effect of noncognitive factors on academic achievement and learning. In this study, we analyzed a set of 31 evaluation instruments designed to measure noncognitive constructs (e.g., self-efficacy, confidence, motivation) within computing education. Using the Lee and Shute framework, we assigned each of the 115 unique constructs found in the instruments into one of the four components (Student Engagement, Learning Strategies, School Climate, Social-familial Influences) and their subcomponents to determine which constructs are most frequently measured. We found that the majority of constructs were designed to measure Student Engagement (Affect and Cognition) and School Climate (Teacher Variables). Constructs measuring Learning Strategies and Social-Familial Influences (e.g., homework strategies, peer influences) occur the least. This study may enable further discussions of what noncognitive factors are/are not currently being measured within the computing education community.

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“…Staff have a critical role in shaping students' beliefs about the importance (or otherwise) of computer programming. Students' beliefs appear to play an important role in how students learn new information: [32]. For example, a meta-analysis of the relationship between self-beliefs and achievement found early self-beliefs correlated positively with later achievement, [33].…”

Section: Personal Views Of Colleaguesmentioning

confidence: 99%

“…Attitudes to programming have been studied elsewhere, e.g. [32]. We are planning a comprehensive study of students' attitudes to computer programming as part of their undergraduate mathematics degree, which will be a separate and self-contained study.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Computer programming in the UK undergraduate mathematics curriculum

Sangwin

O’Toole

2017

International Journal of Mathematical Education in Science and

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This paper reports a study which investigated the extent to which undergraduate mathematics students in the United Kingdom are currently taught to programme a computer as a core part of their mathematics degree programme. We undertook an online survey, with significant follow up correspondence, to gather data on current curricula and received replies from 46 (63%) of the departments who teach a BSc mathematics degree. We found that 78% of BSc degree courses in mathematics included computer programming in a compulsory module but 11% of mathematics degree programmes do not teach programming to all their undergraduate mathematics students. In 2016 programming is most commonly taught to undergraduate mathematics students through imperative languages, notably MATLAB, using numerical analysis as the underlying (or parallel) mathematical subject matter. Statistics is a very popular choice in optional courses, using the package R. Computer algebra systems appear to be significantly less popular for compulsory first year courses than a decade ago, and there was no mention of logic programming, functional programming or automatic theorem proving software. The modal form of assessment of computing modules is entirely by coursework (i.e. no examination).

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Empirical validation and application of the computing attitudes survey

Cited by 74 publications

References 28 publications

Toward More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

Toward More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education

Computer programming in the UK undergraduate mathematics curriculum

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