000			02214nam a22001337a 4500
999			_c64603 _d64600
100			_aSheereen _a15MSSE20 _aSupervisor Dr. Sajida Parveen Soomro
245			_aAcademic Performance Analysis Using Data Mining Methods
260			_aNawabshah: _bQuest, _c2018.
300			_a35p.
500			_aABSTRACT In modem years, Neural Networks is going to spread far and wide and successfully derived in a wide range of data mining applications, often other a lots classifiers surpassing by it. This study aims to examine Neural Networks are a fitting classifier to analysis student performance from Learning Management System data in the context of Educational Data Mining. The dataset used for this study is a Model log file consist of information about 300 students of undergraduate students courses. To analysis the suitableness of Neural Networks, was trained on data obtained during each course. These features were used for training originate from data obtained course and range from usage data like time spent on each course, to grades obtaining for assignments course and quizzes. The Neural Network outperformed of accuracy and is on par with the best classifiers in terms of recall. After training, we able to say that the social networking sites like face book, twitter, whatsapp, we chat, imo etc diverting students from their studies. Students west above time one these social media sites instead of their studies, Hence the ratio of maximum students fall on it by chatting and suffering from Internet for non-educative information. They are gyming to their phones all day mades them lose the sense of time. Cell phones are using by some students, during classes, seminars and also in libraries. The results of the survey were satisfactory based on the training data set as we have applied neural network model to generate the results of the survey though the accuracy may differ based on training data set in this survey we trained 100 records of students and the accuracy were 66% However the tool that we have used to generate the results was rapid miner. It is one of powerful data mining tool.
700			_aDepartment of Information Technology
856			_uhttp://tinyurl.com/44373dex
942			_cTHESIS