ABSTRACT

Developing tools to support students and learning in a traditional or online setting is a significant task in today’s educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student’s performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly per-forming students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students’ grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance.

EXISTING SYSTEM

Creating apparatuses to help understudies and learning in a conventional or internet setting is a huge errand in the present instructive climate. The underlying strides towards empowering such advances utilizing AI procedures zeroed in on anticipating the understudy’s presentation as far as the accomplished evaluations.

PROPOSED SYSTEM

The goal of our work is two-crease. In the first place, to beat this limit, we investigate if ineffectively per-framing understudies can be all the more precisely anticipated by defining the issue as twofold characterization. Second, to acquire bits of knowledge concerning which are the elements that can prompt lack-luster showing, we designed various human-interpretable highlights that evaluate these elements. These highlights were gotten from the understudies’ evaluations from the University of Minnesota, an undergrad public establishment.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10