The research team followed the PRISMA reporting protocol, the international standard for systematic reviews, to identify 163 Scopus-indexed articles on blended learning in programming education published over the 24-year window. Rather than reading each paper in isolation, the authors treated the corpus as a network to be mined. Using the Bibliometrix package in RStudio alongside Python-based preprocessing of the text, they analyzed 477 author keywords, traced co-citation patterns to reveal which works scholars actually build upon, and applied clustering algorithms to group the literature into coherent thematic families. This science-mapping approach, well established in fields such as management and information science, allows researchers to see the intellectual skeleton of a discipline rather than just its surface vocabulary.

Four primary domains emerged from the analysis. The first covers learning models themselves, most prominently the flipped classroom, in which students encounter instructional content such as video lectures before class and devote contact time to problem-solving, along with broader hybrid configurations that mix synchronous and asynchronous delivery. The second domain concerns technology platforms, including massive open online courses and learning management systems built on Moodle, which supply the infrastructure that makes blending practical at scale. The third domain captures persistent pedagogical challenges: sustaining student engagement, scaling instruction to very large cohorts, and designing assessment that works across physical and digital environments. The fourth domain documents interdisciplinary links, connecting programming education to STEM integration, computational thinking, and vocational training.

The temporal story the map tells is striking. Early studies in the 2000s focused on foundational questions of whether blended formats could deliver programming content at all, with researchers experimenting with e-learning platforms, Web 2.0 tools, and multi-sensory approaches. The 2010s brought an explosion of flipped classroom experiments, clicker-based interactivity studies, and investigations of learner characteristics that predict success in blended settings. Notably, some of the most influential work addressed equity and scale, including designs for teaching programming to very large classes in Sub-Saharan Africa, where blended approaches were framed as a response to resource constraints rather than a technological luxury. The COVID-19 pandemic then acted as an accelerant, pushing institutions worldwide into emergency remote teaching and generating a wave of studies on what happens when blending becomes mandatory rather than optional.

To probe the current frontier, the authors conducted a close review of two contrasting samples: the 25 most-cited papers in the corpus and the 25 most recent ones. The comparison exposed a sharp divergence between established theory and emerging practice. The highly cited literature rests on mature pedagogical frameworks, carefully designed interventions, and empirical measures of learning outcomes, engagement, and cognitive load. The most recent papers, by contrast, are dominated by large language models such as ChatGPT, which now feature prominently as tools for generating code explanations, supporting debugging, and providing personalized assistance to novice programmers.

That dominance, the study argues, is running well ahead of the evidence. While LLM-based tools are the subject of intense scholarly and popular enthusiasm, the authors found that empirical research rigorously testing their pedagogical impact in programming education remains limited. Much of the recent literature consists of perceptions, feasibility reports, and speculative frameworks rather than controlled studies measuring whether AI assistance actually improves students’ ability to understand and write code. This matters because programming education has long wrestled with high failure rates and fragile conceptual understanding, and interventions that look promising on the surface can mask shallow learning or encourage over-reliance. Related systematic reviews cited in the study raise concerns that heavy dependence on AI dialogue systems may erode the very critical-thinking and problem-solving skills that programming courses exist to develop.

The tension is not unique to programming instruction, but it is particularly acute there. Learning to program requires learners to construct mental models of abstract machine behavior, to debug their own reasoning as much as their code, and to persist through productive struggle. If a chatbot supplies polished solutions instantly, the formative difficulty that drives learning may simply evaporate. Conversely, well-designed AI scaffolding could offer the kind of individualized, around-the-clock support that blended formats have always promised but rarely delivered at scale. The study’s authors are careful not to declare a winner; their point is that the field currently lacks the empirical foundation to make that judgment, and that the gap between hype and evidence is widening as publication volume surges.

The knowledge map also highlights structural imbalances in the research itself. Studies cluster around introductory courses and higher education, with comparatively less attention to K-12 contexts, professional training, and learners in low-connectivity regions. Assessment remains a weak link: automated grading systems and portfolio-based evaluation appear in the literature, but coherent models for assessing learning across blended environments are still immature. The co-citation analysis further reveals that many recent papers cite a narrow band of foundational works, suggesting that new research may be under-engaged with the field’s accumulated theoretical base even as it chases novel technologies.

For educators and institutions, the study offers a practical consolidation. It confirms that flipped and hybrid models, when thoughtfully designed around active learning, have a substantial evidence trail behind them, and that platforms such as Moodle-based systems and MOOCs are now mature components of the programming education ecosystem. At the same time, it urges caution: the integration of generative AI into blended programming courses should be treated as a hypothesis to be tested, not a settled best practice. The authors call for rigorous empirical evaluation, including controlled comparisons and longitudinal studies, to guide evidence-based adoption of AI tools.

In an era when every syllabus is being rewritten around chatbots, this bibliometric map arrives as both a retrospective and a corrective. It shows how far blended learning in programming education has traveled, from early experiments with online content delivery to today’s AI-infused classrooms, and it identifies precisely where the evidence runs out. The field’s next chapter, the study suggests, should be written not by the fastest adopters but by the most careful measurers, ensuring that the tools transforming how students learn to code are held to the same empirical standard as the pedagogies they are replacing.

Subject of Research: Bibliometric mapping of blended learning research trends in programming education from 2000 to 2023

Article Title: From flip to click: a global bibliometric analysis of blended learning trends in programming education (2000–2023)

Article References: From flip to click: a global bibliometric analysis of blended learning trends in programming education (2000–2023). (n.d.). https://doi.org/10.1007/s43545-026-01760-7

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01760-7

Keywords: blended learning, programming education, bibliometric analysis, flipped classroom, large language models, ChatGPT, MOOCs, Moodle, educational technology, computer science education, AI in education, PRISMA