Automated assessment is well-established within computer science courses but largely absent from human – computer interaction courses. Automating the assessment of human – computer interaction (HCI) is challenging because the coursework tends not to be computational but rather highly creative, such as designing and implementing interactive prototypes. We meet this challenge by developing an automarker for HCI ‘MarCHIr’ to assess key aspects of web prototypes: visual design, interactivity and accessibility. MarCHIr automatically compiles web prototypes and analyses them at the pixel level to assess designs using foundational Gestalt visual principles. While computer science student assessments are often personalised with permutations of numbers, we use random assignment of colours to personalise creative prototype assignments at the scale of hundreds of students. Finally, MarCHIr integrates industry accessibility checks to HCI student assessment. We share 2 years of case study data across two cohorts of university students and reflect on implications for the use of automated assessment within HCI tertiary education.

In recent years, the intersection of music and coding has emerged as a cross-disciplinary approach to engage learners in both musical and computational expression. This article examines the motivations and framing of recent music and coding research through an analysis of 65 articles published between 2004 and 2024. We survey the key findings of this body of work, exploring the extent to which they generalize across diverse populations and educational contexts. We situate these findings within the broader aims of the field of computer science education, critiquing the use of music as a motivational tool in computing education rather than as a topic worthy of exploration in its own right. Methodological limitations are identified, with recommendations for future research to address these gaps. Drawing on theories of onto-epistemic heterogeneity, we call for a more inclusive approach to computing education that integrates sociocultural, historical, and critical perspectives to more effectively support learners from diverse linguistic, cultural, and economic backgrounds.

Computational thinking (CT) is a critical set of 21st century skills that young learners must develop to be successful in contemporary work and life. As more K-12 schools begin to integrate CT into the curriculum, elementary preservice teachers (EPSTs) must be prepared to teach CT to their future students. Therefore, elementary teacher preparation programs should explore ways to incorporate CT into their coursework, ensuring that EPSTs graduate with the necessary knowledge and skills to teach CT. This study investigates the impacts of a three-week CT module implemented in a technology integration course in an elementary teacher preparation program on EPSTs’ CT value beliefs, self-efficacy, and teaching efficacy. The purpose of the module was to introduce EPSTs to basic concepts and big ideas in CT, support their application of CT in block-based coding environments, and promote connection building between CT and authentic real-world scenarios that balance different types of CT activities. Guided by the Interconnected Model of Professional Growth (IMPG), this study examined EPSTs’ learning as a dynamic process of change across the personal and external domains. The module was located in the external domain as a stimulus for learning, while the Teacher Beliefs about Coding and Computational Thinking (TBaCCT) instrument was used to assess changes in value beliefs, self-efficacy, and teaching efficacy within the personal domain. A convergent parallel mixed-methods design was used to analyze pre- and post-surveys administered to 50 EPSTs and semi-structured interviews with four of the EPSTs. The results showed that the beliefs of the CT value of EPSTs evolved and their self-efficacy and teaching efficacy of CT significantly improved after participating in the module, suggesting that the interaction between external learning experiences and internal belief systems - facilitated by reflection and action - contributed to early professional growth. Notably, EPSTs also developed a more accurate understanding of CT, recognized the benefits of both plugged and unplugged CT activities, and expressed a desire for continued CT learning and professional development. Although EPSTs expressed concerns with the scarcity of physical computing toys in schools, they demonstrated a commitment to incorporating CT activities into their teaching and exploring alternative funding avenues to acquire such resources. Practical implications include potential approaches for elementary teacher preparation programs to embed CT into existing curricula.

Higher education institutions are under pressure to educate employable computing graduates who can meet society’s needs. Education, as a process of becoming, shapes students’ identities, and identity is an important topic to study because it influences students’ motivation, retention, and satisfaction. Identity serves as the link connecting an individual student with the social world around them. In the context of computing, this social world includes communities, practices, and values embedded in the computing discipline, as well as institutions that affect the identity development process. This article presents the envisioned identity of computing students as portrayed by Norwegian educational institutions. Through critical document analysis, 29 documents were examined to determine which attributes are portrayed as desirable for computing graduates. The analysis shows that the envisioned computing graduates are portrayed as industry-relevant and employable, continuously competent, socially responsible, and diverse. This study contributes to the literature with a cross-level analysis and a discussion of the phenomenon of envisioned computing identity.

Objectives . This study addresses the challenges faced by students repeating an introductory programming course (CS1) at an online university, where dropout and failure rates remain high. While programming education has been widely studied, targeted interventions for repeating students remain scarce. Our research aims to analyze the experiences of these students and develop a tailored intervention to improve support mechanisms. Participants. The study focuses on students retaking CS1, which were categorized into two groups: those who previously attempted but failed the course, and those who dropped out early with almost no follow-up of the course. Method. An observational approach was used to analyze students’ performance, behavior, and perceptions, combining data from institutional databases and questionnaires. Based on these insights, an intervention was designed, incorporating individualized feedback, structured engagement activities, and encouragement for students to reflect on their past experiences in order to foster a more supportive learning environment. Findings. Initial results indicate that the intervention increased student engagement, particularly in the early weeks of the semester. However, some students continued to struggle with course content, suggesting the need for additional instructional support. While the intervention was effective in fostering engagement, its impact on overall success rates was limited, highlighting the necessity of more personalized approaches. Conclusions. The study provides valuable insights for educators seeking to improve support for repeating students in programming courses. Key lessons include the importance of proactive engagement, tailored feedback, and structured interventions. Future research should focus on developing personalized interventions, validating a tailored questionnaire for deeper insights, and exploring adaptive learning technologies and longitudinal studies to enhance academic outcomes while reducing instructor workload.

Query languages are the foundations of database teaching and education practices. The broad adoption of graph databases contrasts with the limited research into how they are taught. Contrary to relational databases, graph databases allow navigational queries with higher expressivity and lack an a priori schema. In this paper, we design a multi-step exploratory user study investigating these peculiarities and how they impact the student’s learning process. Focusing on the widely used Cypher graph query language, we studied a new taxonomy for classifying the errors around graph queries and conducted an in-depth analysis. Our investigation highlights several learning barriers, including misunderstanding the semantics of language constructs, the confusion between data and schema, and the pattern-matching mechanism. We expect the lessons learned and the derived best teaching practices to have an impact on database education and to influence future generations of students, researchers, and practitioners working on graph database technologies.

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.

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.

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.

Objectives . The increasing demand for computing skills has led to a rapid rise in the development of new computer science (CS) curricula, many with the goal of equitably broadening the participation of underrepresented students in CS. While such initiatives are vital, factors outside of the school environment also play a role in influencing students’ interests. In this article, we examined the effects of students’ perceived parental support on their interest in computer programming and explored the mechanisms through which this effect may have been established as students participated in an introductory CS instructional unit. Participants . This instructional unit was implemented with upper primary (grade 5) school students and was designed to broaden trajectories for participation in CS. The participants in the current study ( N \(=\) 170) came from six classrooms in two rural schools in the western United States. Study Method . The seven-week instructional unit began with students playing a commercial CS tabletop board game that highlighted fundamental programming concepts and transitioned to having students create their own board game levels in the block-based programming language, Scratch. Further, because the board game could be taken home, the instructional unit offered opportunities to involve the family in school-based CS activities. To investigate the effect of students’ perception of parental (specifically father and mother) support on their interest in and self-efficacy to pursue CS, we surveyed students before and after the unit’s implementations and explored the structural relationship of the data using structural equation modeling. Results . We present three findings. First, the combined effect of students’ perceived mother’s and father’s support measured prior to the implementation (pre-survey) predicted students’ self-efficacy (Standardized Estimate ( \({\boldsymbol{\beta}}\) ) \(=\) 0.37, standard error \(=\) 0.010, p \( \lt \) .001) and interest in computer programming (Standardized Estimate ( \({\boldsymbol{\beta}}\) ) \(=\) 0.328, standard error \(=\) 0.134, p \( \lt \) .003) measured after the implementation (post-survey). Secondly, the combined effect of perceived mother and father support (Standardized Estimate ( \({\boldsymbol{\beta}}\) ) \(=\) 0.132, 95% CI [0.039, 0.399], 99% CI [0.017, 0.542]) on students’ interest was mediated by whether or not they took the CS board game home. Conclusions . Our findings indicate that perceived parental support has the potential to play an important role in students’ self-efficacy and interest in computer programming and that providing opportunities for students to bring CS artifacts home has the potential to further affect students’ interest in computer programming.