In personalized education platforms, explainable recommendation is often pursued by transferring knowledge-graph path reasoning methods from other domains, yet differences in educational data and evaluation practices can make these transfers misaligned and leave it unclear which methods remain reliable and why. Knowledge-graph reasoning can enable transparent, structure-aware personalization in this setting by producing recommendation paths …