Students＇　performance　in　university　courses　is　of　great　concern　to　the　higher　education　management　where　several　factors　may　affect　their　performance.　This　paper　addresses　the　capabilities　of　data　mining　and　its　application　in　higher　education　by　offering　a　data　mining　model　to　learn　the　main　attributes　that　may　affect　students＇　performance　in　courses.　During　the　mining　process,　the　CRISP　framework　and　classification　method　for　data　mining　is　used　for　mining　students＇　related　academic　data　over　the　previous　year.　The　findings　can　be　used　to　understand　students＇　learning,　and　then　help　teachers　with　managing　their　class.