Like digital technologies themselves, programming education is embedded in the colonial matrix of power, and access to programming knowledge demands immersion in the epistemologies of the Global North. While there is a growing body of work exploring ways to decolonise programming education, far more needs to be done. Current research focuses on the language of instruction and contextual curricula; outward-facing engagements with decolonisation. However, to move towards digital-decoloniality involves scrutinising how programming knowledge is recontextualised within curricula. Part of the project should be equipping both educators and students with the tools to recontextualise programming itself. To dismantle the colonial logic embedded in programming education, attention must be given to the knowledge formation of the discipline to identify moments of disruption. One such moment is the difficulty students face when recontextualising their mental models of computing, from programming skills to programming concepts. This occurs at the moment of reading, tracing and writing code. Programming requires one to refocus computational thinking and engage with a specific semiotic system, translating the authors’ intention into an executable computational process. Disrupting this moment using the strategies of critical literacies opens computer programming and its resulting code to critical examination, allowing an inward-facing decolonial engagement with the discipline.

This study focuses on the challenges encountered in online cybersecurity education. It adopts an exploratory research design using a mixed-methods approach to investigate the perceptions and experiences of postgraduate students enrolled in an online cybersecurity program. The collection of data is structured into two distinct phases. In the initial phase, qualitative insights are gathered through workshops with students and industry experts, followed by administering a questionnaire to delve deeper into students’ perceptions and challenges. Thematic analysis of the responses reveals significant interest in online cybersecurity programs but also highlights issues such as ineffective communication and poor engagement. The findings highlight the necessity for a standardized framework to improve communication, engagement, and overall effectiveness in virtual learning environments. By empowering instructors to design more interactive courses and leveraging technology efficiently, the framework aims to improve student motivation, satisfaction, and learning outcomes. Moreover, it serves as a valuable resource for institutions, fostering collaboration, and innovation in cybersecurity education. Implementing this framework will create a more conducive learning environment, directly enhancing student preparation for success in the dynamic field of cybersecurity.

As initiatives on AI education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students’ engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this article, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven 2-hour sessions. The summer camp consisted of 2-hour sessions over 4 consecutive days, with 18 students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants’ dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students’ personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.

Understanding software modelers’ difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers’ analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software modeling process, such as incorrect connections or misunderstandings, but neglects the behavioral patterns that can reveal underlying, unaddressed modeling problems. This oversight fails to identify deeper problems that do not manifest as obvious issues but still represent significant potential problems in the software modeling process. Our research concentrates on detecting and classifying problematic modeling behaviors from software modeling process data, revealing the potential problems hidden in the process for MDE education. Specifically, we first construct problematic modeling behavior patterns from three dimensions, including anomalous time intervals, repetitions, and frequencies, to further identify characteristics and priorities relevant to problematic modeling behaviors. Then, we design rules with characteristics and priorities to detect and classify problematic modeling behaviors from problematic patterns. To evaluate the effectiveness of our proposal, we apply it to a data-flow diagram modeling platform. This platform can record modelers’ processes and has been practically used in software engineering courses for five years. We have conducted a case study with 12 participants. The macro F1 of detection and classification problematic modeling behaviors is 82.3%, which shows the effectiveness of our approach. Then, to evaluate the usefulness of our proposal for assisting modeling instructors in MDE education, we conducted another case study with 5 modeling instructors. The results show that our approach can help instructors uncover problems hidden in the software modeling process. The results of two case studies demonstrate that our approach effectively detects and classifies problematic modeling behaviors, enabling instructors to better adjust their instructional plans and improve MDE education.

Pair programming is an effective instructional format in programming education for adolescents. Within pair programming, three potential gender combinations may arise: Boy-Boy (BB), Girl-Girl (GG), and Boy-Girl (BG). This study explores the impact of different gender pairings on the programming self-efficacy and collaborative attitudes of sixth-grade students in rural elementary schools. A total of 82 novices (34 girls and 48 boys) voluntarily formed three types of pairs—BB, GG, and BG—to engage in pair programming using Scratch. The course spanned two months, comprising a total of nine sessions. Surveys on programming self-efficacy and collaborative attitudes were administered to all students after the fifth and ninth sessions. Our research brought to light significant improvements in programming self-efficacy for BB pairs, suggesting that male students learning programming with same-gender partners experienced noteworthy enhancements in their perceived programming abilities. Conversely, students in GG and BG pairs did not display significant changes in their programming self-efficacy. Additionally, all students demonstrated optimal collaborative attitudes when engaged in pair programming with same-gender partners. This study contributes to a more comprehensive understanding of programming self-efficacy and collaborative attitudes within the framework of pair learning. The findings hold the potential to offer valuable insights for educators and curriculum designers aiming to establish inclusive and effective learning environments in rural elementary programming education.

For computing to serve humanity, computing spaces must be safe for all individuals. While prior work has surfaced how hegemonic racial and gendered expectations manifest in computing, it has only indirectly attended to expectations surrounding neurodivergence. As computing stereotypes largely align with stereotypes of some neurodivergent individuals, we investigated whether computing legitimized neurodivergent traits over neuronormative ones. We conducted semi-structured interviews with 21 students, faculty, and industry professionals, sampling both neurodivergent-identifying and non-neurodivergent-identifying participants. We found that computing legitimized hyper-focus, deep “special” interests, and high organization, and that fitting these expectations was frequently required for persistence. Some neurodivergent-identifying participants felt that computing provided refuge from societal neuronormative expectations, though one’s sense of refuge depended on sufficiently fitting computing’s neurodivergent expectations. We offer reflections on inclusion and belonging efforts within computing, as well as directions for future work that attends to individuals’ neurodivergent identities.

Digital literacy is considered to be crucial for social and professional participation. Hence, several projects have been launched in school, as well as extracurricular activities to promote digital literacy in middle school. They aim, among other things, to increase interest in the so-called STEM subjects (science, technology, engineering, and mathematics), which include computer science. Interest in a topic is described in the literature as a crucial factor for learning success and career choice. Still, there is a potential for current research on the expression and distribution of interest in computer science. Therefore, this study aims to address this research gap by examining interest in computer science. The exploration of interest is expanded to include gender aspects and pupils’ dispositions. Given the significantly lower number of women studying in STEM fields, there is a particular focus on an improved understanding of the interests of female pupils as early as middle school. Moreover, the literature suggests that pupils’ dispositions should also be considered in instructional design to achieve stable interest. In this context, the following two dispositions shine out in literature and are therefore focused on in this article: first, interest in contextual, people-related topics (mainly found among women), and second, interest in factual, object-related topics (mainly found among men). In summary, this study investigates how pupils’ interest in computer science is expressed before and after a programming intervention and how it is distributed across gender and dispositions. Therefore, the study surveyed 8th-grade pupils (N \(=\) 114) about their preferences and assessed their interest in computer science before and after an intervention with unplugged materials and playful robots. Results show that all groups of pupils find computer science interesting, and the intervention slightly increased interest. Girls show a marginally lower interest in computer science. Furthermore, the results indicate that interest in computer science did not correlate with interest in contextual or factual dispositions.

Sense of belonging, or belongingness in academia, is an individual's personal conviction as to their acceptance as a valued member of an academic community. The importance of belongingness lies in correlations with motivation, persistence, mental health and well-being. Prior work has shown that belongingness can be lower in students who are minoritised due to their gender, race, nationality, socio-economic status, religion and/or disability. However, there is limited research on how a student's university entry route impacts their sense of belonging in Computer Science and Other Science disciplines. In Ireland's higher education system, university entry is primarily managed through the Central Applications Office (CAO) process, which predominantly serves students transitioning directly from secondary school to university. Additionally, alternative access routes are available for students from socio-economically disadvantaged backgrounds, those with disabilities, and mature students. At University College Dublin (UCD), entry is facilitated through both the traditional direct-entry school leaving route and combined access routes, each serving distinct student demographics. To investigate how these varying entry routes influence students’ sense of belonging, we utilised a survey adapted from the validated ‘Math Sense of Belonging Scale’ to examine the belongingness of undergraduate Science students, including Computer Science students. We examined how the belongingness of students varies by university entry route, and how the belongingness of students in Computer Science varies by university entry route compared to Other Science students. Our results reveal a significant difference in belongingness between students entering university through the direct-entry school leaving route, compared to those who entered via combined access routes (e.g. university admissions schemes for school leavers from socio-economically disadvantaged backgrounds, with disabilities, and mature students). Specifically, within Computer Science, students entering university through the direct-entry school leaving route had significantly higher belongingness than combined access route students. These results provide insight that may help others improve the belongingness of undergraduate Computer Science students.

Objectives . This article explores teacher readiness for introducing artificial intelligence (AI) into Sri Lankan schools, drawing on self-efficacy theory. Similar to some other countries, Sri Lanka plans to integrate AI into the school curriculum soon. However, a key question remains: Are teachers prepared to teach this advanced technical subject to schoolchildren? Assessing teacher readiness is crucial, as it is intricately linked to the overall success of this initiative and will inform the development of appropriate policies. Participants . This study surveyed over 1,300 Sri Lankan public school teachers who teach Information and Communication Technology (ICT) using the snowball sampling approach. The respondents represent approximately 20% of the total ICT teacher population in Sri Lanka. Their readiness to teach AI was assessed using a general teacher self-efficacy scale specifically developed based on the well-established Self-Efficacy Theory. While key demographic factors like gender, education level and educational background were also collected, their impact analysis is not included in this article. Study Method . The study was conducted based on the premise that teachers’ readiness to teach AI hinges on their self-efficacy towards teaching AI in the classroom. This premise was substantiated through a review of existing research, and a conceptual model of teachers’ self-efficacy for AI instruction was developed. To assess this model, a nationwide survey targeting school ICT teachers was conducted. The questionnaire used in the survey was based on existing research on evaluating teacher self-efficacy. Data analysis, focusing on testing and validating the conceptual model, primarily employed the PLS-SEM approach. Findings . This study identified several key findings: (1) Teachers generally reported low self-efficacy regarding their ability to teach AI; (2) Teachers’ self-efficacy was most influenced by their emotional and physiological states, as well as their imaginary experiences related to teaching AI; (3) Surprisingly, mastery experiences had a lesser impact on their self-efficacy for teaching AI; and (4) Neither vicarious experiences (observing others teach AI) nor verbal persuasion had a significant impact on teachers’ self-efficacy. Additionally, the study revealed that the teachers’ own level of expertise in AI, along with their social capital, is insufficient to deliver a standard AI curriculum. Conclusions . The analysis of the results found that Sri Lankan teachers currently lack the readiness to teach AI in schools effectively. Potential lapses in certain sources of self-efficacy were also identified. It further revealed the need for a more systemic approach to teacher professional development. Therefore, the study recommends further research exploring the potential of incorporating a socio-technical systems perspective into the government’s teacher training initiatives.