Students’ Study Performance Prediction using Predictive Models

Abstract

With the advancement of technologies students are getting a great help in their study. Except
classroom studiesthey are taking advantage of online MOOCs and blended courses. Various
educational institutions are using LMSs that help to collect enormous data about students’ online
behavioral. Data about students’ behavior can also be collected using Intelligent tutoring system that
easily monitor student’s displacement in any task. A brief description of various online tools available
for predicting student’s behavior have been given in this paper. However,different tools are used to
monitor the patterns related to students’ behavior and theirlearning habits and how it effect their
study performance. The most popular tool to analysis students’ behavior is Weka that provides all
algorithms for predicting students’ performance. Sending learning piles to students using mobile
phone after lectures is also very effective. Cross class predictions provide a way to judge at risk
students at an early stage that helps in completing online courses. Blended courses like webinars
provide learners an interactive way to learn. Students’ behavior is extensively beneficial in predicting
students’ performance, as the students who do all the work in time like submit assignments, practical
e-tests and other material in time to the instructor are having more chances to proceed. Predictive
learning analytics provide great help to instructorincrease course quality by providing students
effective study material. It provides great help to students to improve their performance.