A Case Study on English as a Second Language Speakers for Sustainable MOOC Study

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.

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn

“…In addition to the pre-processed text data, some of the extracted features from our previous work [ 11 ] have been used in order to compare the performance of the algorithms. The extracted features used in the developed deep learning model are categorised in two areas, Step Activity and Comments, presented in Table 6 .…”

“…In a previous, proof of concept study, we exploited commonly used machine learning algorithms to predict performance of participants categorised into the different language groups according to their first language in a single language MOOC on FutureLearn [ 11 ].…”

“… Consistent model accuracy irrespective of commenting behaviour: The deep learning model trained with only extracted features was also trained and tested for each language group. The accuracy of the model is almost same (84%) for each language-based group of learners, whereas their behaviour in commenting was quite different [ 11 ]. Generalisability of our approach: The model with only comments was trained with the seven instances of a language MOOC delivered on FutureLearn.…”

Implementation of MOOC platforms into teaching English to IT specialists

“…Moreover, MOOCs allow to follow the basic principles of foreign language teaching: deduction, student orientation, purposefulness and the communicative content of lessons [27]. I. Duru et al [28] classify studies on investigating behaviours of second language learners within MOOCs as:…”

Defining and Measuring Completion and Assessment Biases with Respect to English Language and Development Status: Not All MOOCs are Equal

“…Indeed, Cho et al 2017 [26] argued that there is very little evidence on how English as a second Language (ESL) participants study in MOOCs. On that, Turkay et al [5] provided large-scale evidence of the reduced odds of ELLs to obtain a certificate, and Duru et al [27] provided small-scale evidence on the reduced completion rate among ELLs. Another factor that is known to be associated with low learning achievements in MOOCs (and in other contexts) is low SES [11].…”