Understanding students’ reasoning in algorithmic problem solving (APS in short) remains challenging. This work integrates outcome-based and process-oriented analyses to characterize reasoning in graph-based APS, revealing breakdowns in algorithm adaptation and conceptual modeling. Building on an empirically grounded framework, ongoing work designs and evaluates scaffolded interventions, informed by prototyping and pilot studies, to support conceptual model development and adaptive expertise.