Challenges of Identifying Second Language English Speakers in MOOCs

Duru, İsmail; Sunar, Ayşe Saliha; Doğan, Gülüstan; White, Su

doi:10.1007/978-3-319-59044-8_22

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In our previous study [38], we detected that some participants declared that their first language was different from the official language(s) of the country they live in.…”

Section: Categorising Mooc Participants Based On First Languagesmentioning

confidence: 91%

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

et al. 2019

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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.

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“…In our previous study [38], we detected that some participants declared that their first language was different from the official language(s) of the country they live in.…”

Section: Categorising Mooc Participants Based On First Languagesmentioning

confidence: 91%

“…Our previous approach, manually going through all discussion threads was very time consuming and not scalable [38]. The new approach exploits regular expression patterns and similarity metrics to identify the information about country, city, nation, and language in the discussion.…”

Section: Automatic Detection Of Esl Participants With Natural Languagmentioning

confidence: 99%