Evaluating　student　learning　effect　plays　an　essential　role　in　education,　which　is　typically　done　by　assessing　student＇s　final　deliverables.　However,　the　student＇s　learning　process　has　not　been　properly　explored　in　the　past.In　this　paper,　we　propose　an　interactive　student　learning　effect　evaluation　framework　which　focuses　on　in-process　learning　effect　evaluation.　In　particular,　our　proposal　analyzes　students　modeling　assignment　based　on　their　operation　records　by　using　techniques　of　frequent　sequential　pattern　mining,　user　behavior　analysis,　feature　engineering,　and　process　mining.　A　comprehensive　online　modeling　platform　has　been　developed　to　enable　data　collection.　We　have　carried　out　a　case　study,　in　which　we　applied　our　approach　to　a　real　teaching　scenario,　consisting　of　student　online　modeling　behavior　data　collected　from　24　students　majoring　in　computer　science.　We　also　associate　our　process　mining　results　with　the　numeric　evaluation　values.　The　preliminary　result　of　case　analysis　has　shown　good　potential　to　mine　student　modeling　patterns　and　interpret　their　behaviors,　contributing　to　student　learning　effect　evaluation.