Prediction of Users’ Professional Profile in MOOCs Only by Utilising Learners’ Written Texts

Abstract

Identifying users’ demographic characteristics is called Author Profiling task (AP), which is a useful task in providing a robust automatic prediction for different social user aspects, and subsequently supporting decision making on massive information systems. For example, in MOOCs, it used to provide personalised recommendation systems for learners. In this paper, we explore intelligent techniques and strategies for solving the task, and mainly we focus on predicting the employment status of users on a MOOC platform. For this, we compare sequential with parallel ensemble deep learning (DL) architectures. Importantly, we show that our prediction model can achieve high accuracy even though not many stylistic text features that are usually used for the AP task are employed (only tokens of words are used). To address our highly unbalanced data, we compare widely used oversampling method with a generative paraphrasing method. We obtained an average of 96.4% high accuracy for our best method, involving sequential DL with paraphrasing overall, as well as per-individual class (employment statuses of users).