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Many students struggle when they are first learning to program. Without help, these students can lose confidence and negatively assess their programming ability, which can ultimately lead to dropouts. However, detecting the exact moment of student struggle is still an open question in computing education. In this work, we conducted a think-aloud study with five high-school students to investigate the automatic detection of progressing and struggling moments using a detector algorithm (SPD). SPD classifies student trace logs into moments of struggle and progress based on their similarity to prior students' correct solutions. We explored the extent to which the SPD-identified moments of struggle aligned with expert-identified moments based on novices' verbalized thoughts and programming actions. Our analysis results suggest that SPD can catch students' struggling and progressing moments with a 72.5% F1-score, but room remains for improvement in detecting struggle. Moreover, we conducted an in-depth examination to discover why discrepancies arose between expert-identified and detector-identified struggle moments. We conclude with recommendations for future data-driven struggle detection systems.