Measurement of Self-regulated Learning: Strategies for mapping trace data to learning processes and downstream analysis implications

Trace data provides opportunities to study self-regulated learning (SRL) processes as they unfold. However, raw trace data must be translated into meaningful SRL constructs to enable analysis. This typically involves developing a pattern dictionary that maps trace sequences to SRL processes, and a trace parser to implement the mappings. While much attention focuses on the pattern dictionary, trace parsing methodology remains under-investigated. This study explores how trace parsers affect extracted processes and downstream analysis. Four methods were compared: Disconnected, Connected, Lookahead, and Synonym Matching. Statistical analysis of medians and process mining networks showed parsing choices significantly impacted SRL process identification and sequencing. Disconnected parsing isolated metacognitive processes while Connected approaches showed greater connectivity between meta-cognitive and cognitive events. Furthermore, Connected methods provided process maps more aligned with cyclical theoretical models of SRL. The results demonstrate trace parser design critically affects the validity of extracted SRL processes, with implications for SRL measurement in learning analytics.