01738nas a2200265   4500000000100000000000100001008004100002260001200043653000900055653001500064653002400079653001800103653000800121653001600129653001200145100002300157700002100180700002000201245009700221856009500318300001000413490000600423520102900429022001401458       2018                            d  c09/201810aWeka10aPrediction10aStudents’ Success10aDecision Tree10aJ4810aRandom Tree10aREPTree1 aAlaa Khalaf Hamoud1 aAli Salah Hashim1 aWid Aqeel Awadh00aPredicting Student Performance in Higher Education Institutions Using Decision Tree Analysis  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_2_3_pdf_30196.pdf  a26-310 v53 aThe overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms.  a1989-1660