A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the studentsxe2x80x99 performance during their study years. This helps tailoring an appropriate learning strategy for different groups.  This work develops a novel framework for a mobile app to predict the studentsxe2x80x99 performance before starting the Universitiesxe2x80x99 education. The framework is built on a Universityxe2x80x99s students data from year 20092017. It has three main components, namely, a neural network model that predicts the GPA, a mobile App that tests basic knowledge in different domains, and a fuzzy model that estimates the future studentsxe2x80x99 performance.

Leveraging Neural Network for Online Learning Performance Prediction and Learning Suggestion Learning performance analysis is such a research field that draws much attention from researchers though it has just been emerged in recent years. On the one hand, analyzing learning behaviors can help learners to choose their learning methods and allocate their study time in a more appropriate way. On the other hand, learning analysis can provide valuable feedbacks for teachers and administrators to improve teaching efficiency and quality. This paper studies and analyzes more than 640,000 learning data from the MOOC platform edX. A treebased model along with an information gain measure is applied to identify the usefulness of data features. A backpropagation neural network model is further adopted to train data and achieve a prediction model of learning performance. In addition, a genetic algorithm calculates learning score conditions and return feedbacks as suggestions to learners. Experiment results demonstrate the effectiveness of the utilization of the methods in the predication of online learning performance.