• Praveen Mishra

The watched low quality of alumni of some Universities lately has been halfway followed to insufficiencies of the National University Admission Examination System. In this examination an Artificial Neural Network (ANN) model, for foreseeing the probable execution of a competitor being considered for entrance into the college was created and tried. Different components that may probably impact the presentation of the Student were recognized. The present arrangement of assessing Student execution isn't possible and it has been seen that it regularly prompts disappointment among the Student, as without right indicators of achievement in instructive foundations, Student and organizations put accentuation on mistaken indicators and put time and assets in. This examination utilized Student information stored in a Moodle server and anticipated Student achievement in course, in view of four learning exercises - correspondence by means of messages, community oriented substance creation with wiki, content connection estimated by records saw and self-assessment through online tests. Next, a model dependent on the Multi-Layer Perceptron Neural Network was prepared to foresee Student execution on a mixed adapting course condition. The model anticipated the presentation of Student with right order rate, ROR, of 99.3%.

Artificial Neural Networks, Artificial Intelligence, Higher Education, Prediction of Students’ Performance Learning Analytics, Student Achievement.

"Student Performance Prediction By Employing ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.427-431, February-2019, Available :http://www.jetir.org/papers/JETIREO06100.pdf

"Student Performance Prediction By Employing ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp427-431, February-2019, Available at : http://www.jetir.org/papers/JETIREO06100.pdf