Publication Date

Spring 2020

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Katerina Potika

Second Advisor

Suneuy Kim

Third Advisor

Nada Attar

Keywords

Learning analytics, predicting student success, visualization, machine learning techniques

Abstract

The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to monitor and self-assess their progress throughout their studies and periodically perform a self-evaluation. From the edu- cators’ perspective, predicting student grades can help them be proactive, in guiding students towards areas that need improvement. Moreover, this study also takes into consideration social factors that might affect students’ performance.

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