Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms

Abstract

The conceptualisation of self-regulated learning (SRL) as a process that unfolds over time has influenced the way in which researchers approach analysis. This gave rise to the use of process mining in contemporary SRL research to analyse data about temporal and sequential relations of processes that occur in SRL. However, little attention has been paid to the choice and combinations of process mining algorithms to achieve the nuanced needs of SRL research. We present a study that 1) analysed four process mining algorithms that are most commonly used in the SRL literature – Inductive Miner, Heuristics Miner, Fuzzy Miner, and pMineR; and 2) examined how the metrics produced by the four algorithms complement each. The study looked at micro-level processes that were extracted from trace data collected in an undergraduate course (N=726). The study found that Fuzzy Miner and pMineR offered better insights into SRL than the other two algorithms. The study also found that a combination of metrics produced by several algorithms improved interpretation of temporal and sequential relations between SRL processes. Thus, it is recommended that future studies of SRL combine the use of process mining algorithms and work on new tools and algorithms specifically created for SRL research.