Students　behavior　in　the　classroom　has　a　close　relation　with　the　academic　performance　of　students.　With　the　widespread　use　of　mobile　phones,　the　students　are　easily　distracted　by　mobile　phones　during　the　class.　Therefore,　analyzing　the　students　behavior　in　the　classroom　becomes　important　to　improve　the　quality　of　teaching.　However,　the　existing　methods　for　analyzing　students　behavior　in　the　classroom,　such　as　facial　recognition,　has　low　efficiency　with　large　amounts　of　irrelevant　data.　And　it　also　raises　concerns　about　privacy　and　ethical　issues.　To　solve　this　problem,　in　this　paper,　a　novel　mobile　software　called　EasyClass　has　been　developed　to　collect　and　quantify　students　behaviors.　Based　on　the　data　collected　by　EasyClass,　we　propose　to　utilize　user　profiling　to　study　students　behavior　in　the　classroom.　The　clustering　algorithm　is　firstly　employed　to　classify　the　data　and　generate　semantically　tags　which　describe　students　behavior　in　the　classroom.　Then　a　sequence　and　association　rule　analysis　method　is　used　to　analyze　the　behavior　tags　and　generated　association　rules.　The　results　enable　educators　and　non-expert　users　to　gain　a　deeper　understanding　of　students　behavior　in　the　classroom,　and　further　improve　the　quality　of　the　classroom　teaching.　©　2023　IEEE.