Large language models (LLMs) are increasingly used in educational settings where decisions require factual, pedagogical, and value judgments. Existing benchmarks assess general value alignment or educational capability, but rarely evaluate whether model decisions are consistent with a specified value framework in situated educational contexts. We present EduValueBench, an expert-led benchmark of 1,696 decision-oriented items with expert reference decisions and rationales, covering 12 core values in China, seven educational scenario families, and three challenge mechanisms. We evaluated 15 LLMs from China-based providers using a multi-LLM evaluator panel. Target LLMs received only the educational question, without value labels or expert references. Against the expert-authored reference decisions, model scores range from 24.7% to 54.1%, while value- and scenario-level profiles differentiate similarly ranked models. Case analyses show that responses may be fluent and seemingly reasonable yet still optimize a different educational objective or overlook ethical boundaries. Among responses with jointly valid labels, pairwise evaluator agreement ranges from 88.2% to 92.0%. EduValueBench provides a culturally situated diagnostic benchmark for comparing unguided model decisions with expert-authored educational references. Code and data are available at https://github.com/tjunlp-lab/EduValueBench.