Learning Analytics: Analysis of Methods for Online Assessment

Abstract

Assessment is a fundamental part of teaching and learning. With the advent of online learning platforms, the concept of assessment has changed. In the classical teaching methodology, the assessment is performed by an assessor, while in an online learning environment, the assessment can also take place automatically. The main purpose of this paper is to carry out a study on Learning Analytics, focusing in particular on the study and development of methodologies useful for the evaluation of learners. The goal of this work is to define an effective learning model that uses Educational Data to predict the outcome of a learning process. Supervised statistical learning techniques were studied and developed for the analysis of the OULAD benchmark dataset. The evaluation of the learning process of learners was performed by making binary predictions about passing or failing a course and using features related to the learner’s intermediate performance as well as the interactions with the e-learning platform. The Random Forest classification algorithm and other ensemble strategies were used to perform the task. The performance of the models trained on the OULAD dataset was excellent, showing an accuracy of 95% in predicting the students’ learning assessment.