Qualitative Parameter Triangulation: A Conceptual and Methodological Framework for Event-Based Temporal Models
Yeyu Wang, Zack Carpenter, Zachari Swiecki, David Williamson Shaffer · 2025
Learning is a complex process that occurs over time. To represent this complex process, interests has been rising in conceptualizing and integrating temporality into model constructions. However, the construction of an event-based temporal model is challenging. Specifically, researchers struggle with translating qualitative heuristics and theoretical hypotheses into quantifiable temporal parameters. Existing methods of parameter derivation also suffer from issues of model transparency and oversimplification of learning contexts. Thus, we proposed a conceptual and methodological framework, Qualitative Parameter Triangulation (QPT), to center human interpretation in model construction. Based on human interpretations, QPT constructs a qualitative loss function and derives temporal parameters using an automatical optimization algorithm. The final step is to check consistency between a global representation with local qualitative evidence given specific learning moments. By presenting a worked example of QPT, we demonstrated the process of maintaining pairwise alignments across interpretation, systematization, and approxi-gation. As a proof of concept, QPT is a feasible framework for determining temporal parameters and constructing event-based temporal models.