Computing Education

Computing education encompasses the study and practice of teaching and learning computing concepts, tools and methods across formal and informal settings. At its core it addresses how individuals develop digital skills—from programming and algorithmic thinking to data analysis—and how educators design curricula and pedagogies that foster computational proficiency, critical engagement with technology and lifelong learning. Recent decades have seen a shift from teaching ‘about’ computers towards empowering learners to create, evaluate and shape digital artefacts. This transformation has been driven by advances in interactive learning environments, online platforms, open resources and collaborative experiences such as hackathons. Research in the field examines cognitive processes underlying programming, equity and diversity in access, ethical dimensions of data and algorithms, integration of computing across disciplines and effective teacher professional development. Global case studies highlight the role of co-designed tools, contextualised projects and analytical dashboards in supporting personalised feedback and formative assessment. A growing emphasis on theoretical frameworks and rigorous empirical methods underpins efforts to map learning trajectories, measure skill acquisition and inform policy, ensuring that computing education responds to technological change and societal needs.

Research from Nature Portfolio

Recent studies have extended established innovation frameworks to explore students’ engagement with emerging generative AI tools in higher education. By integrating organisational, technological and environmental factors with knowledge application, researchers have modelled how network quality, system responsiveness and social influence shape satisfaction and digital skill development. Findings indicate that students’ adoption intentions are strengthened through practical exposure to AI-driven language models, supportive institutional cultures and peer collaboration, suggesting pathways for embedding generative AI literacy into computing curricula.

Research from all publishers

Complementary work in other journals has proposed a transversal ethical framework for higher education research methods. It argues that training in data ethics must move beyond procedural consent to interrogate power relations embedded in digital technologies and guide educators in embedding critical reflection on data collection, use and community impact. In parallel, the DALI Data Literacy Framework introduces four interrelated dimensions—understanding data, acting on data, engaging through data and ethics/privacy—that together support emancipatory and context-sensitive data practices. Further research on algorithm literacy situates it within media and information literacy, defining competences for demystifying algorithmic processes in social platforms and proposing learner-centred interventions to enhance critical evaluation of automated decision-making and address disinformation.

Computing Education publication trend

The graph below shows the total number of articles in computing education across all publications each year (not limited to Nature Index journals).

Technical terms

Critical data literacy: The ability to analyse, question and apply data within socio-technical contexts, recognising biases, ethical implications and power structures inherent in data practices.

Algorithm literacy: Competence in understanding, critically evaluating and interacting with algorithmic processes that influence digital content and decision-making.

Computational thinking: A problem-solving approach that employs concepts from computer science such as abstraction, decomposition and pattern recognition to formulate and test solutions.

Formative assessment: Ongoing evaluative practices that provide learners with feedback and support adaptive instructional strategies to improve learning outcomes.

References

1. Computers and Education – Recognising Opportunities and Managing Challenges.
2. Analyzing ChatGPT adoption drivers with the TOEK framework. Scientific Reports (2023).
3. Reframing data ethics in research methods education: a pathway to critical data literacy. International Journal of Educational Technology in Higher Education (2023).
4. Developing the DALI Data Literacy Framework for critical citizenry. RIED Revista Iberoamericana de Educación a Distancia (2023).
5. Algorithm Literacy as a Subset of Media and Information Literacy: Competences and Design Considerations. Digital (2024).