Unraveling success in online learning: a ladderpath analysis of MOOCs learner interaction patterns

Purpose This study aims to develop and apply a systems-based analytical methodology for understanding the complexity of learner-content interactions in Massive Open Online Courses (MOOCs). Traditional linear analysis methods primarily focus on predictive modeling for learning outcomes but fail to capture the underlying patterns and dynamic nature of learning interactions. The research seeks to identify behavioral patterns associated with successful learning outcomes and provide insights into how learners construct and reconstruct their interaction patterns in online learning environments. Design/methodology/approach The study employed the ladderpath methodology to analyze over 435,000 log entries from 363 participants in an open online course. Interaction complexity was quantified through two key metrics: ladderpath-index (representing meaningful interaction modules) and order-index (representing interaction quantity). The approach leverages information theory principles to characterize interaction behaviors on a large scale, focusing on the hierarchical organization and reuse of behavioral components rather than simple sequential patterns. Findings Results reveal that successful learners who earned certificates exhibited higher overall interaction quantities, more consistent interaction patterns and less reliance on exploratory behaviors than unsuccessful learners. Specifically, successful learners demonstrated lower ladderpath-rates and higher order-rates, indicating a strategic balance between exploration and routine patterns. These findings challenge assumptions that more varied interaction necessarily leads to better outcomes, suggesting instead that structured and strategic interaction patterns are key to learning success in MOOCs. Originality/value This study introduces a novel, universally applicable method for analyzing learner-content interaction in online learning environments. The ladderpath approach offers a unique perspective by quantifying interaction complexity through information theory, enabling large-scale characterization of learning behaviors. Unlike traditional methods that often require context-specific frameworks, this approach has broader implications for open online learning across various courses and topics. By providing a systemic view of interaction complexity, the study contributes to the field of social physics in education, potentially uncovering fundamental patterns in online learning. This research paves the way for more comprehensive and generalizable analyses of learner behaviors in diverse online educational settings.