Explaining Heterogeneity in Learner Performance with Digital Learning Material

Digital technologies have become integral to contemporary education, permeating schools, universities, and lifelong learning. Learning management systems, multimedia resources, adaptive platforms, and generative AI now structure how instruction is delivered and experienced. However, empirical evidence on the effectiveness of digital instruction remains strikingly heterogeneous: even in comparable domains, studies report widely differing learning outcomes. This variability cannot be understood by focusing on technologies or surface design features alone. Instead, it points to the crucial role of underlying learning processes and learner characteristics. Cognitive, metacognitive, affective, motivational, and social processes, as well as individual differences in prior knowledge, self-regulation, and dispositions, shape how learners engage with digital instruction. A process-oriented and person-oriented perspective is therefore needed to explain why digital learning outcomes differ and how instructional design can better account for this diversity.The central problem this Research Topic addresses is the unexplained diversity of digital learning outcomes. Despite the widespread use of digital instruction across comparable subject domains and educational levels, empirical findings range from strong positive to negligible or even negative effects. This suggests that treating “digital learning” as a uniform phenomenon is no longer adequate. Instead, we need to understand which instructional designs work, for whom, through which processes, and under which conditions. To achieve this, the Research Topic aims to bring together studies that (a) identify mediating processes (e.g., cognitive, metacognitive, affective, motivational, social) through which digital instruction exerts its effects, and (b) examine moderating learner characteristics (e.g., prior knowledge, self-regulation, traits, digital competences) that shape these effects. By integrating process data, learner variables, and design features, the contributions should develop more precise explanatory models and design principles for digital instruction that account for, rather than average out, the diversity of digital learning outcomes.This Research Topic focuses on process-oriented and person-oriented explanations of digital learning outcomes. We invite submissions that examine: (a) cognitive, metacognitive, affective, motivational, social, and cultural processes as mediators of digital instructional effects; (b) learner characteristics (e.g., prior knowledge, self-regulation, traits, digital competences, socio-demographics) as moderators; and (c) the interplay of design features, processes, and learner variables in explaining heterogeneous outcomes across digital formats (e.g., LMS-based courses, multimedia resources, simulations, game-based environments, adaptive/AI-supported systems).We welcome Original Research (including experimental, quasi-experimental, correlational, longitudinal, design-based, and learning-analytics studies), as well as Systematic or Scoping Reviews, Conceptual and Theoretical Articles, and Methods Articles introducing innovative ways to capture and analyse digital learning processes, mediators, and moderators. All manuscripts should explicitly link instructional design, learner characteristics, and process-based explanations of outcome variability. Topic editors: Maik Beege, Sascha Schneider Source: https://www.frontiersin.org/research-topics/84918/explaining-heterogeneity-in-learner-performance-with-digital-learning-material