The widespread availability of generative tools has weakened a long-standing assumption in computing education: that the production of working code can serve as a proxy for student competence. In resource-constrained settings, these tensions are compounded by intermittent power, high data costs, and emergent institutional governance. We report a two-site qualitative study of Nigerian computing departments (n = 20), drawing on semi-structured interviews with students and academic staff and analysing the corpus through thematic analysis to characterise assessment practice under policy-light conditions. Our findings describe a persistent detection trap, where staff rely on inconclusive software or subjective judgement, and institutional silence, where expectations for acceptable use are unevenly specified and applied. We contribute the Scaffolded AI-Verification Framework (SAVF), presented as a traceable design pattern catalogue of handset-first, low-data feasible teaching moves derived from these stakeholder accounts. SAVF comprises (i) permitted-help statements with disclosure, (ii) process-evidence bundles that foreground explanation and testing, and (iii) course-anchored prompts that require adaptation to local materials and constraints. We provide three pattern specifications, a traceability table linking themes to requirements and patterns, and adoption guidance for low-bandwidth implementation, positioning SAVF as a stakeholder-informed design contribution with a testable evaluation plan for future in-situ study rather than as an evaluated intervention.

Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. This working group will consider the ethical and societal impacts of GenAI in higher computing education. In this paper, we outline the goals, methodology and expected deliverables of the working group. In particular, we will carry out a systematic literature review to address a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and social impacts, we will provide an evaluation of university policies on the adoption and guidelines for use of GenAI for computing education and develop a framework to outline the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for educational and professional codes of ethics.

This research delves into the dynamics of active school transport (AST) by utilizing a two-stage co-design process and leveraging persuasive technology within a game for promoting AST called Battleship-AST. The primary aim of this research is to thoroughly investigate the two-stage game co-design process employed in creating a Battleship-AST game. Moreover, our research aims to evaluate participants’ perceptions regarding the motivating and engaging potential of the persuasive technology and gamification features embedded within the final iteration of the game. This evaluation aims to understand how these features influence participants’ motivation to increase their usage of AST through gameplay. In pursuit of these objectives, the research builds upon the existing Battleship-AST prototype and actively engages school children in a collaborative two-stage co-design process. Their valuable insights and preferences were harnessed in refining the game, which was subsequently tested during a tech event in Skellefteå, Sweden. The findings shed light on various aspects of the game’s impact, from its reception to the gamification features integrated within. Notably, the research highlights the positive impact of the co-design process, with increased motivation and engagement observed among the participants. Their involvement in shaping the game’s design resulted in a more engaging and enjoyable experience. The persuasive technology features, encompassing competition, collaboration, auditory cues, a virtual reward system, and an emphasis on similarity, played a pivotal role in sustaining engagement and motivating players. Elements such as rewards, leaderboard progression, and badges proved highly effective in encouraging continued participation and fostering a positive feedback loop. However, the study also identifies areas for potential improvement, including the need to measure real-life progress and refine the game’s levelling system. The research indicates that refining feedback mechanisms and tailoring game content to individual preferences could create an even more engaging experience. Additionally, long-term playtesting is proposed to assess the game’s extended impact. The findings offer promising avenues for enhancing motivation and engagement in AST, which can contribute to the promotion of healthier and more sustainable transportation choices among school children.

Abstract This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

Artificial Intelligence (AI) for K-12 education has been considered a global initiative. However, evidence of Africa’s inclusion in globalization across schools is lacking in the literature. Besides, resources, including materials and content, are developed across Hong Kong, Japan, Europe, and the USA. These suggest that contextualized resources are effective for AI implementation in schools. Since appropriate pedagogical approaches, sound instructional methods, materials, tools, and activities familiar to the student for instruction lead to effective learning, we embark on a literature survey to unravel the approaches and kind of AI resources utilized across contexts. A systematic literature review methodology was used in this paper to understand the trends of teaching AI at the K-12 educational level. Scientific databases such as IEEE, ACM, Web of Science, and Scopus were searched to gather relevant literature in tandem with our research aim. Out of the 451 articles that were retrieved, only 54 fit well into the inclusion criteria and were reviewed for further analysis. This study revealed several existing approaches and resources used to teach AI in schools.

This research paper examined theories, pedagogics, and contents explored by studies focusing on promoting computational thinking (CT) in higher education institutions (HEIs). CT has become a fundamental approach to building problem-solving skills, which requires a thought process. The field of CT is generally still maturing, and the use of CT as an approach to introduce freshmen to introductory programming courses in HEIs has been gaining scholars' interest in the recent past. To appreciate the strategies explored to promote teaching and learning of CT in HEIs; evaluate scholarly discussions, contributions, and potential impact of studies in this field, there is a need to ground the theoretical constructs that build the foundation for the field. A literature review methodology was adopted in this study. The data collected from the Web of Science, the Scopus, ACM, and ProQuest databases were analyzed to provide answers to the research questions. The findings from this study suggest that constructionism and constructivism are the prevailing learning theories explored by scholars in this field to deploy CT in HEI. Additionally, the study revealed that activity-based learning, problem-based learning, automatic assessment-based learning, and self-regulated or self-reflective learning are the prominent pedagogies used by educators. These findings provided a strong foundation for research in this growing field. Besides, the findings also create an opportunity for positioning CT in HEI's educational curriculum regarding how CT should be taught in that context.

The educational community continuously seeks ways to improve the learner-centered learning process through new approaches like Learning analytics and its dashboard, which is helpful to enhance the teaching and the learning process. It involves a process whose final goal is presenting results to support decision-making about improving the learning process. However, a descriptive Learning analytics interface for analyzing learning data of students, including the disadvantaged, where to view and interpret learners' historical data is -in general- missing in this research domain. Hence, more research is still required to establish the philosophy of learning analytics on inclusion with an interface for the stakeholders to understand learning and teaching in an inclusive learning environment. This paper fills this gap by providing an inclusive educational learning analytics dashboard to support teachers and students. This study aimed to present a learning analytics implementation in the context of a smart ecosystem for learning and inclusion. We gave the inclusive educational needs and discussed the workflow followed during the descriptive learning analytics dashboard development. Therefore, the study improved existing learning analytics dashboards with a descriptive approach and inclusiveness of students with disabilities. Owing to the software development nature of this study, agile methodology based on five stages was applied: requirement elicitation; data gathering; design and prototyping; implementation; and testing and integration. We performed an initial evaluation, which indicated that the dashboard is suitable for understanding teachers' and students' needs and expectations. Besides, the visualization of inclusive learning characteristics improves engagement and attainment of learning goals.

The benefits of teaching machine learning to K-12 pupils include building foundational skills, useful mental models and inspire the next generation of AI researchers and software developers. However, introducing machine learning in schools has been a challenge even though several initiatives, curriculum design, platforms, projects, and tools exist to demystify the concept. The existing resources are scattered and sometimes overlap. Thereby selecting the appropriate tools to adopt in teaching becomes an arduous task for the teachers and other practitioners. More so, despite the increasing number of papers published in this field, there are still gaps in identifying specific tools and resources for teaching machine learning in K-12 settings. This study presents a literature review on machine learning in K-12 by selecting articles published from 2010 to 2021. Therefore, this paper presents a resource catalog and surveys of tools to help teachers find suitable teaching paths and make the decision to introduce activities that help students understand the basic concepts of machine learning. Based on the research objective, we utilized six databases to extract relevant information, while thirty-nine peer-reviewed articles were collected based on a systematic literature search and were analyzed. This study identified resources, tools, and instructional methods as the main categories of pedagogical items needed to ensure impactful teaching of machine learning in K-12 settings. Besides, the mode of operation, benefits and the challenges of the pedagogical tools for teaching machine learning in K-12 settings were unraveled. The findings also show the increased number of initiatives resulting in tools development to support machine learning teaching. Finally, this study provides recommendations for future research directions to help researchers, policymakers, and practitioners in the education sector identify and apply various resources to aid decision-making in practice and to democratize machine learning practices in schools.

The momentum around computational thinking (CT) has kindled a rising wave of research initiatives and scholarly contributions seeking to capitalize on the opportunities that CT could bring. A number of literature reviews have showed a vibrant community of practitioners and a growing number of publications. However, the history and evolution of the emerging research topic, the milestone publications that have shaped its directions, and the timeline of the important developments may be better told through a quantitative, scientometric narrative. This article presents a bibliometric analysis of the drivers of the CT topic, as well as its main themes of research, international collaborations, influential authors, and seminal publications, and how authors and publications have influenced one another. The metadata of 1,874 documents were retrieved from the Scopus database using the keyword “computational thinking.” The results show that CT research has been US-centric from the start, and continues to be dominated by US researchers both in volume and impact. International collaboration is relatively low, but clusters of joint research are found between, for example, a number of Nordic countries, lusophone- and hispanophone countries, and central European countries. The results show that CT features the computing’s traditional tripartite disciplinary structure (design, modeling, and theory), a distinct emphasis on programming, and a strong pedagogical and educational backdrop including constructionism, self-efficacy, motivation, and teacher training.

This study is a work in progress that aims to design and implement a smart learning environment based on virtual reality technology to aid the teaching and learning of programming concepts. The paper followed the approach of designing and modelling of requirement specification for the intended smart platform. This modelling approach is desirable in satisfying activities that engender the design and prototyping of the smart learning environment based on the design science research method. The study discusses the proposed architecture of the system, modelled the system with UML, presents a scenario-based model for teaching and learning of programming concepts, and connect the outcome to the future research.

This research Full paper presents the pedagogies of machine learning in K-12. The new learning pedagogies and technologies are introduced with the aim of enhancing student engagement, experience and learning outcome. This study examined how machine learning has been taught in the recent past and further explores the ways and suitable approaches for K-12 context. Literatures on pedagogies associated with machine learning were reviewed to understand the dynamics and suitability of these pedagogies to support machine learning teaching. Though studies have explored pedagogies for machine learning in higher education context, few studies explored pedagogical strategies for teaching machine learning in K-12. In all, the pedagogies employed in teaching and learning of machine learning has not witnessed much research in literature. The pedagogical strategies revealed in the literature are mostly adopted in the higher education institutions to enable the of teaching machine learning concepts. The literature survey revealed several pedagogical strategies such as problem-based learning, project-based learning and collaborative learning used in higher education institutions. The revealed pedagogies suggest learners-centered approaches such as active learning, inquiry-based, participatory learning, design-oriented learning among others will be suitable for teaching machine learning in K-12 settings.

This paper investigates the impact of puzzle-based learning technique (PbLT) for teaching introductory programming in the context of Nigeria. The participants in the study were computer science students at Usmanu Danfodiyo University, Sokoto, Nigeria. The study adopted the quantitative research method. The study shows that PbLT has a positive impact on the students' understanding of introductory programming. In addition, the students confirmed that MobileEdu-Puzzle learning application was supportive and easy to use. Nonetheless, the result revealed that information technology infrastructure especially internet connectivity is a major challenge that may hinder the use of mobile learning technologies such as MobileEdu-puzzle. The overall outcome shows that PbLT holds a lot of promise towards enhancing students learning experiences.

M-learning plays a progressively significant role in the advancement of teaching and learning in higher education. However, the effective implementation of m-learning in higher education will be based on users' experiences and motivation to use this technology. Though m-learning has become global, developing countries such as Nigeria are yet to enjoy the full potential offered by m-learning. This study is focused on ascertaining students' experiences with m-learning, determining the influence of m-learning on students' motivations and interests, and identifying factors that are limiting m-learning adoption in Nigeria. We investigated these experiences by analysing questionnaires collected from undergraduate and postgraduate students of six universities in Nigeria. The results from our study show that the students own and use diverse mobile devices to engage in educational activities and other social networking purposes. Some of these learning activities are sending SMS messages, playing educational games, social learning, reading e-books/pdfs, and completing assignments and quizzes. Students expressed their satisfaction with m-learning especially for supporting them to learn anywhere, anytime. They further confirmed that m-learning motivates, interests, and inspires every aspect of learning. Furthermore, the students acknowledges that the interactivity, flexibility, convenience and engagement of m-learning were authentic learning experiences.