<![CDATA[This study formulates Sequential Microtelemetry as a framework for representing process evidence in physics problem solving within a virtual laboratory. Event-to-feature rules are specified for later empirical validation. Five candidate indicators are Systematic Variable Manipulation Ratio (SVMR), Evidence-Based Revision Rate (EBRR), Productive Rerun Ratio (PRR), Evidence-Conflict Recovery Rate (ECRR), and Result-Revisit Rate (RRR). In the present study, these indicators were not extracted from event logs; instead, they were generated directly at the participant level in 120 synthetic records to examine Models A-B-C, a participant-feature alignment permutation control, and random-seed sensitivity. Mean R² values for Models A, B, and C were 0.465, 0.521, and 0.622, respectively; permuting feature alignment reduced R² to 0.494. One hundred random-seed replications preserved the B>A and C>B performance ordering. The results verify the behavior of the downstream modeling layer under synthetic feature-level conditions, not event-to-feature extraction or empirical validity in students. The contribution is a physics-semantic specification that links digital events, temporal order, derived markers, and auditable process indicators.]]>
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