Abstract

Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants’ backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants’ language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn “Understanding Language: Learning and Teaching” MOOC using natural language processing. We compared algorithms’ performance when extracting discernible features in participants’ engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.

Highlights

  • Massive Open Online Courses (MOOCs) are increasingly recognised as providing high-value learning resources enabling an accessible route to sustaining the expansion of both formal and informal education

  • This work fills in this important gap in MOOC research that can be useful for MOOC providers to identify different groups of English language learners according to their primary languages

  • A more recent study where findings were based on data from international participants in a MOOC focussed on social enterprise education, Calvo et al [43] identify linguistic and cultural barriers as inhibiting learner’s access to MOOCs

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Summary

Introduction

MOOCs are increasingly recognised as providing high-value learning resources enabling an accessible route to sustaining the expansion of both formal and informal education. Tailored tools, based on the needs and motivation of English as a second language speaker (ESL) participants could be more effective In another example, Colas et al [9] show that discussion forums in additional languages result in an improvement in engagement being observed. This work fills in this important gap in MOOC research that can be useful for MOOC providers to identify different groups of English language learners according to their primary languages. It provides a detailed comparative analysis on behaviours and performances of learners in different English language groups which has not been addressed by Sustainability 2019, 11, 2808 any study yet to the best of our knowledge.

Learning Analytics in MOOCs
Engagement in MOOCs from Language Perspectives
Research Aim and Methodology
Categorising MOOC Participants Based on First Languages
Course Engagement Analysis
Behaviours in Course Steps
Following Behaviours
Course Performance Prediction
Feature Extraction
Balancing Data
Implementation and Prediction Results
Weekly Prediction of Course Completion for Early Intervention
Discussion and Conclusions
Findings
Future Work
Full Text
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