Purpose This study aims to examine whether a structurally adaptive e-learning path can improve the efficiency of large-scale digital skills training while maintaining comparable learning outcomes relative to a regular, non-adaptive path. Specifically, it investigates (1) differences in time on task and learning outcomes between adaptive and regular conditions across seven European Computer Driving Licence (ECDL)/International Certification of Digital Literacy (ICDL) modules and (2) whether the two instructional conditions differ in their underlying performance structure and student performance profiles. Design/methodology/approach The study involved 2,064 upper-secondary students enrolled in seven online ECDL/ICDL courses delivered via Moodle. Students were assigned to an adaptive path or a regular full-content path through controlled randomisation stratified by school year and gender. In the adaptive condition, pre-test results unlocked only the learning activities needed to address identified knowledge gaps; in the regular condition, all activities were mandatory. Learning efficiency was assessed through time saved, time attended and topics skipped, while learning effectiveness was assessed through first-attempt pass rates and post-test scores. For structural analyses, we also computed a composite performance index combining achievement and time efficiency, and used confirmatory factor analysis, multi-group modelling and TwoStep clustering to examine latent performance patterns. Findings The adaptive path produced substantial efficiency gains across all seven modules, markedly reducing instructional time and content exposure. Learning effectiveness results were more mixed: outcomes were broadly comparable across several modules, the adaptive group performed significantly better in Computer Essentials and the regular group performed significantly better in Word Processing and Spreadsheets. Overall, the findings indicate a strong efficiency advantage for the adaptive condition without uniform improvement in effectiveness. The course-level performance indicators loaded on a common latent performance factor, although the relative contribution of individual courses differed across conditions. Cluster analysis identified three performance profiles, including a smaller sub-group of consistently low-performing learners present in both conditions. Research limitations/implications The study is limited to one national context, one learning management system and one digital skills curriculum. Pre- and post-assessments reused the same item pools, which may have introduced practice effects. In addition, the performance index and clustering analyses were restricted to successful completers, so these structural findings should be interpreted as applying to completers rather than to the full randomised sample. Future research should test similar designs in other subject domains, including behavioural and motivational measures, and explore richer adaptive logics for practice-oriented modules. Practical implications The findings suggest that structurally adaptive learning paths can meaningfully reduce time-to-mastery in large-scale digital training. However, the mixed effectiveness results across modules indicate that adaptive sequencing should be calibrated to course characteristics and complemented, where needed, by additional pedagogical support for lower-performing learners. Social implications By increasing the efficiency of school-based digital skills training, adaptive e-learning may help education systems deliver certification-oriented programmes under realistic time and resource constraints. Originality/value The study contributes large-scale evidence on a scalable form of structural adaptivity implemented in an authentic school context. Its main contribution lies not in proposing a novel adaptive algorithm, but in testing whether pre-test-based adaptive sequencing can reduce time-to-completion at scale while preserving broadly comparable outcomes, and in linking this question to latent-variable modelling and learner-profile analysis.
Pagano et al. (Mon,) studied this question.