The change in the English computing curriculum and the shift towards computer science (CS) has been closely observed by other countries. Female participation remains a concern in most jurisdictions, but female attainment in CS is relatively unstudied. Using the English national pupil database, we analysed all exam results (n=5370064) for students taking secondary school exams in 2016 focusing on those students taking GCSE computer science (n=60736) contrasting this against ICT (n=67359). Combining gender with ethnicity and poverty indicators, we find that poorer females are significantly more likely to take computing than richer females. CS is popular amongst ethnic minority females with white girls showing the worst proportional representation of all the female ethnic groups. ICT was far more equitable for females and poorer students than CS. CS females typically get better grades than their male peers. However, when controlling for average attainment in other subjects, males get 0.31 of a grade higher. Female relative underperformance in CS is most acute amongst large female cohorts and girls studying in mixed gender schools. Girls do significantly better than boys in English when compared to CS, supporting theories around female strengths lying outside STEM subjects. The move to introduce CS into the English curriculum and the removal of ICT look to be having a negative impact on female participation and attainment. Using the theory of self-efficacy we argue that the shift towards CS might decrease the number of girls choosing further computing qualifications or persuing computing as a career. Computing curriculum designers and teachers need to carefully consider the inclusive nature of their computing courses.
Abstraction is one of the most fundamental ideas in computer science (CS), and as such, according to Bruner, it should be taught spirally, starting as early as possible and revisited at every level of education. However, teaching this concept to novices is a very complicated task, as has been emphasized by many CS and mathematics education experts. A framework for teaching abstraction to novice students in the context of algorithmic problem solving was introduced by Armoni . We studied the effect of this framework in an introductory CS course for 7th-graders, in which Scratch was used as the programming language for implementing algorithmic solutions. Our findings indicate that the framework was effective for developing CS abstraction skills as well as other related skills and aspects, such as the volume and quality of script documentation, the use of initialization processes, and the perception of the nature of CS. It also significantly improved students general CS knowledge as well as their programming skills.
This paper expands on knowledge of computing identity by building on what is known about prior identity models in science and mathematics education. The model theorizes three primary sub-constructs that contribute to the development of a computing identity: belief in one's performance/competence, interest, and recognition in computing. Drawing on data from a nationally representative survey of more than 1,700 college students at 22 colleges and universities, the study tested the alignment of the theorized model to the measures on the survey. Confirmatory Factor Analysis was used to validate whether the appropriate measures loaded on the three separate sub-constructs. Predictive validity was also established by testing whether the computing identity measures predicted the choice of a computer science career. The results reveal that a computing identity proxy based on the theorized measures was a highly significant predictor of students' computer science and information technology career choice (p<0.0001). In addition, this work also established criterion-related validity by showing gender differences that had been found by prior work in computing. Finally, the theorized measures were found to be reliable and internally consistent. The educational understanding of computing identities may provide an important tool to help researchers and practitioners improve student persistence in computer science.
This paper is a systematic review of work in the computing education literature on plagiarism. The goal of the review is to summarize the main results found in the literature and highlight areas that need further work. Despite the the large body of work on plagiarism, no systematic reviews have been published so far. The reviewed papers were categorized and analyzed using a theoretical framework from the field of Fraud Deterrence named the "Fraud Triangle". According to this framework, fraudulent behavior occurs when the person is under "pressure", perceives the availability of an "opportunity" to commit fraud and "rationalizes" the fraudulent behavior in a way that makes it seem not unethical to him or her. The review found the largest amount of the reviewed papers to discuss ways for reducing the "opportunity" to plagiarize, as well as tools for detecting plagiarism. However, there is a clear lack of empirical work evaluating the deterrent efficacy of these strategies and tools. The reviewed papers also included mentions of a wide range rationalizations used by computing students when justifying plagiarism, the most important of which are rationalizations that stem from confusion about what constitutes plagiarism. Finally, work on the relationship between "pressure" in computing courses and plagiarism was found to be very scarce and incommensurate with the significant contribution of this factor to plagiarism.
In response to the growing call to bring the powerful ideas of computer science to all learners, education decision makers, including teachers and administrators, are tasked with making consequential decisions on what curricula to use. Often, these decision makers have not been trained in computer science and are unfamiliar with the concepts taught and tools used. This is especially true in K-12 contexts where computer science expertise is less prevalent. To aid in the decision-making process around computing curricula, this paper introduces the TEC Rubric. The TEC Rubric is comprised of three main categories: Teacher Accessibility, Equity, and Content designed to support educational decision makers and designers when it comes to computing instruction. Along with presenting the full rubric and the process used in its creation, this paper describes two examples of the rubric in action. First, the TEC Rubric is used to evaluate two widespread computer science curricula to demonstrate its evaluative capacity highlighting differences between the two curricula. Second, we show how the TEC Rubric can be used to help inform the design of new K-12 computing curricula. Overall, the TEC Rubric is designed to serve as a useful resource in the ongoing quest to bring effective, equitable, and engaging computing instruction into schools around the world.