As　education　is　taking　an　increasingly　significant　role　in　society　today,　efficient　and　precise　evaluation　of　student　learning　effect　is　calling　for　more　attention.　With　recent　advances　of　information　technology,　learning　effect　can　now　be　evaluated　via　mining　student’s　learning　process.　This　paper　proposes　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,　and　feature　engineering.　In　order　to　enable　effective　student　learning　evaluation　and　deliver　practical　value,　we　have　developed　a　comprehensive　online　modeling　platform　to　collect　operation　data　of　modelers　and　to　support　the　corresponding　analysis.　We　have　carried　out　a　case　study,　in　which　we　applied　our　approach　to　a　real　dataset,　consisting　of　student　online　modeling　behavior　data　collected　from　24　students　majoring　in　computer　science.　The　results　of　our　analysis　show　that　our　approach　can　effectively　and　practically　mine　student　modeling　patterns　and　interpret　their　behaviors,　contributing　to　assessment　of　their　learning　effect.　©　Springer　Nature　Switzerland　AG　2019.