While global interest in K–12 AI and ML education grows, many African education systems lack foundational computing education beyond basic computer literacy. This creates unique challenges for AI integration in countries where computer science isn't part of the K–12 curriculum. Teachers are central to this effort, but little is known about what motivates them to engage with these technologies or how they use them. This study examined what motivates K–12 teachers to engage with AI and ML in Botswana. Using a mixed-methods approach, we surveyed 59 teachers using an adapted version of the Motivation to Teach Computer Science (MTCS) scale and open-ended questions. We used Self-Determination Theory (SDT) as a lens to interpret the findings. Results showed that intrinsic motivation and identified regulation were primary drivers. Context-specific, extrinsic factors were also observed, including a desire to improve educational systems and concerns about infrastructure in schools. Access disparities in teachers' use of AI emerged: secondary and computing teachers with better infrastructure used AI tools more frequently than primary or non-computing teachers. The results show that while teachers' engagement with AI stems from perceived teaching and learning value, sociocultural factors like infrastructure determine how motivation translates into practice. These findings have implications for professional development, infrastructure planning, and inclusive AI adoption in resource-constrained education systems.

As computing education expands globally, the dominance of English in programming languages can present barriers for students who are not native English speakers. This research work investigates whether language matters in programming by examining how a bilingual programming workshop influences student attitudes, perceived understanding and comfort. Sixty bilingual participants from San Jose State University, 40 with little to no prior programming experience and 20 experienced programmers completed a two-hour workshop using the multilingual Hedy platform. Pre- and post-surveys measured changes in confidence, enjoyment, motivation, identity, and usefulness, complemented by open-ended questions about participants' experiences. All students showed improvements across most constructs, but inexperienced students gained more in confidence and enjoyment, while experienced students showed smaller gains and preferred coding in English. Qualitative responses revealed that while most students felt English supported clarity, bilingual students more often reported that using both languages helped with conceptual understanding, despite some translation challenges. These findings contribute to ongoing discussions in computer science education about linguistic inclusion and offer practical insights for educators designing multilingual or culturally responsive programming environments.

This ITiCSE Working Group (WG) has two goals: first, to increase awareness of computing education research in the African countries, and second, to create and strengthen connections between computing education researchers in African countries and those in the larger computing education research community.

There is a worrying lack of published work in international venues about computer science education (CSE) in the Global South, especially in Africa. Despite the increasing number of African institutions offering various computer science degree programs to meet the growing demand for computing skills and practices, there is limited scholarly evidence in the CSE context. As a result, it is important to understand why there is low representation of the continent in computing education research (CER) space. In this paper, we provide insights into the factors hindering the growth of CER in Africa, including strategies that could be employed to increase participation. This study employed a qualitative approach through the use of interviews. This study gathered the thoughts of ten computing education researchers across different countries and regions of Africa. The findings uncovered African researchers' motivation for CER and challenges such as limited awareness, financial constraints, and inadequate institutional support. Proposed solutions include establishing collaborative networks, raising awareness, and providing funding opportunities to boost CER output in the region. This research provides valuable insights for CSE, contributing to inclusion, equity, and broadening participation in computing.

In recent years, significant interest in AI has resulted in many K-12 learning resources being developed for teachers and students. However, a consensus on which concepts and skills should be developed has yet to be reached. In addition, there is limited research on how these resources can be used by teachers. As AI/machine learning is not currently mandated in many CS curricula, supporting teachers is an ongoing challenge. Analysing resources currently available to teachers provides us with a starting point to consider how resources could be designed to better support teaching and learning in this area. In this paper, we surveyed the landscape of resources available and categorised 307 AI/ML teaching resources based on their core learning focus. We devised and employed a learning content analysis framework, SEAME, to determine how resources covered AI at multiple levels: (i) Social & Ethical; (ii) Application; (iii) Model, and; (iv) Engine. We found that most resources focused on the Application level (80%) with many covering the Model level (60%) and fewer at the Social & Ethical (40%) and Engine (27%) levels. We found little consensus about what to teach and how with many resources failing to specify target ages. Similarly, we found few examples of resources with professional development opportunities or appropriate lesson documentation, indicative of the challenges teachers face in teaching about AI. We propose that the SEAME framework provides an innovative starting point for teachers and researchers to review resources and consider what a progression of AI concepts and skills might look like that is comprehensive and simple to use. Likewise, it provides a common language for articulating the learning focus of resources. In order to support teachers encountering a new strand of CS, we suggest that further work in developing age-appropriate curricula and better documentation for AI resources is needed.

No abstract available.

When novice programming students already know one programming language and have to learn another, what issues do they run into? We specifically focus on one or both languages being functional, varying along two axes: syntax and semantics. We report on problems, especially persistent ones. This work can be of immediate value to educators and also sets up avenues for future research.

As computing education grows rapidly across the globe, there is an increasing need to broaden participation and engage all students in computing, particularly those from underrepresented groups and developing countries. A programming workshop that uses various interventions to broaden participation was set up to empower African university students with computer programming skills to address this need. Out of 487 applications, 172 participants from 11 African countries were selected to participate in the workshop. This paper aims to explore the participants' experiences, including their motivation for attending the workshop, their programming skills confidence, what they found most useful for their learning, and the challenges they faced. Employing a mixed-methods design, our quantitative and qualitative results indicate that participants' motivations were more intrinsic. Furthermore, the results indicate that participants' confidence increased after the workshop. They found the hands-on sessions with the tutors to be most beneficial to their learning. We also observed that many participants struggled with access to basic ICT resources during the workshop, even though they were provided with the internet. Our findings highlight that participants are interested in learning programming; therefore, to support them, sustainable collaborative partnerships are necessary to provide relevant teaching interventions and resources.

We describe the development and trial of a survey based on self-determination theory to investigate the motivations of K-12 teachers to learn ML and AI. Our participants (n=28) were most motivated by personal enjoyment (an intrinsic motivator) and student benefits rather than extrinsic factors, such as external pressure. We will further investigate teacher motivation, as we suggest that this is an important aspect of ML and AI education research.

There is currently an existing gap between Computer Science Education (CSE/CSEd) academia and the industry (e.g., government, profit, non-profit organizations, and research centres). It was evident in the findings for our recent research, "Identifying Opportunities and Potential Roadblocks for CSEd Professionals," presented at the ICER 2021. We discovered that the industry is unaware of CSE as a discipline, and qualification requirements for jobs relevant to CSEd exclude CSEd expertise. On the other hand, our study revealed that graduate students are interested in industry jobs. Still, their advisors discourage them from considering such employment. This disarticulation negatively impacts students, academia, and industry. Graduate students are left with little or no information and guidance to develop their career pathways and use their learned skills. Innovative research from academia is developed as theory without application to the industry. Also, the industry does not benefit from the CSE research and expertise. This Birds-of-Feathers (BOF) will bring together academic faculty members, graduate students, and industry representatives to bridge the existing gap by discussing the challenges and potential solutions. For additional information, visit https://www.csedgrad.org/birds-of-a-feather.

The recent growth of computing education globally has resulted in a growing number of Computer Science Education (CSEd) graduate students. To support and make a global impact in computing education, there is a need for these graduates to be in a diversity of careers/roles both within and beyond academia. Currently pursuing a CSEd PhD requires a leap of faith that one can overcome issues not only associated with pioneering a new discipline within the host institution but also is often undertaken without knowing what career opportunities will be available upon graduation. Surveys conducted in Spring 2020 and 2021 with graduate students and advisors document these challenges [3].

Motivation More and more high schools are teaching programming, and in many cases, teachers teach multiple programming languages to the same group of students. Objectives The goal of this paper is to explore the views of high-school teachers on second and subsequent programming languages, including their motivation for teaching multiple languages, their struggles, and their use of transfer strategies when they teach their second or third programming language. Method The study consists of semi-structured interviews with 23 high-school teachers in two European countries. Results Our findings indicate that school pupils face the same issues as university students when moving from first to subsequent languages. Furthermore, the teachers’ attitudes towards second language learning are highly variable, both positive and negative, with some supportive teaching strategies used, but many less helpful ones in evidence too. Discussion Our findings suggest that the value of second language learning needs to be highlighted in teacher professional development materials more strongly and that teachers might need more support in implementing transfer strategies.

Full Paper. Research. We discuss possible ways to direct students to right level of introductory programming. While many schools offer college preparatory or advanced placement courses in computing, there is still, unfortunately, a large part of the "college-ready" population that has no opportunity to learn computing at all before they arrive. Regulation of CS education at the state/province or national level is still rare (but growing). Thus incoming students possess a wide range of skills and knowledge. When coupled with increasing enrollments, this diversity of experience can result in courses having large numbers of both absolute beginners and seasoned coders. Such courses are difficult to teach, intimidate novice students, and bore those with more experience. This can result in low engagement and retention.Unlike mathematics and language arts, introductory courses in CS vary widely from one institution to another in both conceptual material and programming language used. A standard point of entry to college mathematics is a calculus course, with some students instead starting earlier with pre-calculus or an algebra refresher, and others starting out in the second-term calculus course. There is rarely a concern about student skill being hidden by notational or other language differences, because the language of mathematics is close to universal. Similarly, freshman language arts courses in reading and/or writing assume a certain level of skill and maturity of comprehension and expressiveness in the target language; otherwise remedial courses are provided.We investigate placement of incoming first year students into appropriate introductory computer science courses at higher education institutions where there is more than one choice of first course. The goal is to determine the best way to decide which first course would be the most helpful for each student.

Prior research has shown that students face transition challenges between programming languages (PL) over the course of their education. We could not find research attempting to devise a model that describes the transition process and how students' learning of programming concepts is affected during the shift. In this paper, we propose a model to describe PL transfer for relative novices. In the model, during initial stages of learning a new language, students will engage in learning three categories of concepts, True Carryover Concepts, False Carryover Concepts, or Abstract True Carryover Concepts; during the transition, learners automatically effect a transfer of semantics between languages based on syntax matching. In order to find support for the model, we conducted two empirical studies. Study 1 investigated near-novice undergraduate students transitioning from procedural Python to object-oriented Java while Study 2 investigated near-novice postgraduate students doing a transfer from object-oriented Java to procedural Python. Results for both studies indicate that students had little or no difficulty with transitioning on TCC due to positive semantic transfer based on syntax similarities while they had the most difficulty transitioning on FCC due to negative semantic transfer. Students had little or no semantic transfer on ATCC due to differences in syntax between the languages. We suggest ways in which the model can inform pedagogy on how to ease the transition process.

It is a natural part of a student's computing education to transfer from language to language, hence adopting to a new programming language (PL) quickly is a necessary skill. Prior work in computer science research mainly brings awareness of the success and difficulties that students face when learning new languages. In addition, work that directly relates to PL transfer mainly concerns experienced programmers problem solving in a new language, evidencing plan transfer. We could not find research attempting to devise a model of PL transfer based on code comprehension. We explore this phenomenon in the context of five university students transitioning from procedural Python to object-oriented Java, over a period of 10 weeks. We analyse the results through the lens of a model of second language acquisition using the notion of Semantic transfer and the Mindshift learning theory (MLT). The findings indicate that during the initial learning stages, learners relied mostly on their syntactic matching between Python and Java and subsequent semantic transfer which affected their learning positively on Carryover concepts and negatively on Changed concepts. Students could not transfer their semantic knowledge on concepts they perceived as Novel. An understanding of the transfer process learners go through during a shift can help inform our pedagogy on how to ease the transition process and achieve an effective learning process, and we provide pointers in this direction.

As students learn computer science (CS), they will need to transfer skills and understanding from one programming language (PL) to another. Prior research has explored the transition between languages for (mainly experienced) programmers, identifying a number of challenges. I could not find research attempting to devise a model that describes how students' learning of programming concepts is affected during the shift between languages. I propose the first draft of a model to describe PL transfer for relative novices based on the literature and my observations of these students transitioning from procedural Python to Java. In the model, concepts in the new language may be Carryover, Changed or Novel; during the transition, learners automatically effect a transfer of semantics between languages based on matches made between the syntax of the two languages.

We propose a working group to investigate methods of proper placement of university entrance-level students into introductory computer science courses. The main issues are the following. The ability to predict skill in the absence of prior experience The value of programming language neutrality in an assessment instrument Stigma and other perception issues associated with students' performance, especially among groups underrepresented in computer science The impact or potential impact on underrepresented populations (minorities, those with lower socioeconomic status) The outcomes/satisfaction/retention metrics in the major of the paced/tracked students compared to those in one-size-fits-all introductory classes