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The main purpose of this study is to examine the implementation of active methodologies in the teaching–learning process in computer science. To achieve this objective, a systematic review using the PRISMA method was performed; the search for articles was conducted through the Scopus and Web of Science databases and the scientific search engine Google Scholar. By establishing inclusion and exclusion criteria, 15 research papers were selected addressing the use of various active methodologies which have had a positive impact on students' learning processes. Among the principal active methodologies highlighted are problem-based learning, flipped classrooms, and gamification. The results of the review show how active methodologies promote significant learning, in addition to fostering more outstanding commitment, participation, and motivation on the students' part. It was observed that active methodologies contribute to the development of fundamental cognitive and socio-emotional skills for their professional growth. [ABSTRACT FROM AUTHOR]

There is a nationwide effort to increase the representation and engagement of minoritized students in computer science education. Discourse about the barriers to diversity among computer science majors is often characterized by student pathologies and does not consider the impacts of classroom culture and instructor pedagogies. Amid the push for strategies to recruit and retain minoritized students in computer science, little has been done to transform curriculum and analyze faculty perspectives on curriculum and pedagogy as methods to increase students' access to the computer science major. This paper presents an example of curriculum redesign for an undergraduate introductory computer science course (ICS) that sought to address issues of inequitable representation by centering student identities and redistributing power in favor of students. The authors draw upon critical sociocultural and the 4Es heuristic for disciplinary literacy to reimagine the ICS course as a space that centers on the important roles of identity and power in solving for diversity in computer science education. We highlight for researchers and practitioners how our work may be used to disrupt meritocratic practices that alienate minoritized and economically disadvantaged students and to expand definitions of mastery and expertise in computer science education. [ABSTRACT FROM AUTHOR]

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple-choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities. [ABSTRACT FROM AUTHOR]

Popular discourses of computing and computer science can often frame the sector and the people within it as highly intelligent yet socially challenged, contributing to stereotypes that can potentially exclude those perceived to lack these skills or characteristics. For young people, such stereotypes can influence their educational and career aspirations, especially if there are discrepancies between their own identity and perceptions of what is desirable for computer science students. Drawing on open-ended questionnaire data from 3235 young people in England (aged 11–16), we collected 9442 keywords that students used to describe their “ideal student” in computer science education. An understanding of these perceptions allows us to recognise the prevailing stereotypes that may be shaping young people’s views and aspirations. In this paper, we employ an innovative large-scale descriptive analysis of the most common words that students use to describe the ideal computer science student, including its differences, if any, by students’ demographic background (e.g. gender, ethnicity, free school meal) and responses to the questionnaire (e.g. aspirations to be a computer scientist). We tentatively identified eight clusters of popular ideal student characteristics, namely being Smart & Clever; Knowledgeable & Interested; Determined & Hardworking; Kind & Helpful; Creative; Independent; Confident; and Collaborative. By examining how students imagine the ideal student in computer science, we gain better understanding of their educational aspirations and choice of study and provide educators with valuable insights to potentially challenge and reshape these perceptions. These insights can inform educational interventions to foster a more inclusive computing education. [ABSTRACT FROM AUTHOR]

This article presents the usage of Integrated Course Design (ICD) in the design and evaluation of applying agile methodologies within an undergraduate module of study to foster the development of computer science students employability skills. Undergraduate programs of computer science typically follow traditional educational methods which can lead to students unable to connect knowledge learned in class to actual situations and students are often assessed individually, whereas collaborative group projects are more akin to industry practice. The teaching experience reported gives students the opportunity to relate concepts learnt in class to a practical group-based project. Students must meet the requirements of a 'client' who will provide feedback and additional challenges for students while following the Agile framework SCRUM. Positive student feedback and module grades 7.70% higher than the department average over a four year period indicates the teaching structure and assessment presented is an effective method to foster the development of technical and soft skills of undergraduate computer science students. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]

As ethical issues involving computer technologies and social media become more common, there is increasing interest in what role ethics should play in computer science education. As a result, computer science departments worldwide have ramped up their efforts to examine relevant ethical issues in undergraduate computer science classes to prepare emerging professionals to face relevant issues when they enter the computing workforce. As part of these efforts, a public R-1 Hispanic-Serving Institution located on the United States–Mexico border piloted a leadership course based on the Relational Leadership Model (Komives et al., 2013). This model provides a broad idea of leadership that focuses on leaders developing and exercising their ethical awareness by engaging in discussions of ethical issues. The pilot course was organized around the implementation of a cooperative pedagogical tool known as structured academic controversy (Johnson et al., 1996). We describe in detail the strategy for implementing this approach, discuss key elements of students' final reflections about their participation in the academic controversy, and present the quantitative results to examine students' understanding of leadership and satisfaction with the pilot course. [ABSTRACT FROM AUTHOR]

Computer science teaching standards for grades K-8 have been implemented in nearly all U.S. states, and the core subject area teachers (e.g., math, science, English, social studies) have been asked to integrate these standards into their instruction. Thus, it is important that K-8 pre-service teachers of all subjects are both prepared and motivated to teach computer science concepts—such as programming—upon entering the field. However, little is known about how pre-service teachers learn and retain programming knowledge or obtain and sustain their motivation related to programming. Maker-focused educational robotics activities have the potential to both reduce abstract cognitive load and work as motivational tools for STEM learning. The purpose of this study was to examine pre-service teachers' motivational persistence and retention of programming concepts after learning with educational robotics through maker-focused computer science activities. Hands-on maker robotics programming activities were used to teach and motivate pre-service teachers. This quantitative study utilized repeated measures through pre-, post-, and 6-month follow-up surveys and tests. The findings indicated the pre-service teachers' programming comprehension gains exhibited on the posttest deteriorated substantially to near-baseline levels within 6 months of instruction. Conversely, pre-service teachers' motivation related to programming continued to rise after the instruction had concluded. Both the retention of comprehension of programming concepts and motivational persistence findings imply that educator preparation providers should integrate programming instruction throughout their pre-service teacher curricula and support curricular initiatives that call for the integration of computer science instruction across pre-service teacher methods courses to reinforce computer science learning. [ABSTRACT FROM AUTHOR]