How Do English Language Learners Interact with Different Content Types in MOOC Videos?

Uchidiuno, Judith Odili; Koedinger, Ken; Hammer, Jessica; Yarzebinski, Evelyn; Ogan, Amy

doi:10.1007/s40593-017-0156-x

Cited by 19 publications

(23 citation statements)

References 26 publications

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“…We refer to this framework and demonstrate, to our knowledge for the first time, the relation between course as a whole and learning achievements, using a largescale cross-course analysis. A research that is closely related to ours is [28], who focused on how ELLs interacted with videos. We focus on interactive assessment and completion rates, and study whether there is evidence that the biases related to these variables are course-related.…”

Section: Course Design and Learning Achievementsmentioning

confidence: 96%

“…Instructional design that supports the needs of ELLs while learning in a MOOC is required. Resource-oriented approaches that focus on making videos -the primary learning resource in MOOCs [4] -more adapted to ELLs, were presented in [28,29]. However, this issue was not explored with relation to assessment, which plays a key role in the learning process of MOOC students [17,16].…”

Section: Language Development Status and Learning In Moocsmentioning

confidence: 99%

“…Language for example is inferred by assuming that learners speak the primary language in the country from which they connect to the platform [33,4]. Other practices for learners language inference include using the web browser language preferences [28], or analyzing the combination of declared locality and keyword search within forum posts [27]. In a similar fashion, inference of development status for each learner (HDI), is done via IP geolocation mapping and is available at the country level resolution [31].…”

Section: Inferring Demographics Traits Of Mooc Learnersmentioning

confidence: 99%

See 2 more Smart Citations

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

Gershon¹,

Ruipérez‐Valiente²,

Alexandron³

2021

Preprint

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The emergence of Massive Open Online Courses (MOOCs) broadened the educational landscape by providing free access to quality learning materials for anyone with a device connected to the Internet. However, open access does not guarantee equals opportunities to learn, and research has repetitively reported that learners from affluent countries benefit the most from MOOCs. In this work, we delve into this gap by defining and measuring completion and assessment biases with respect to learners' language and development status. We do so by performing a large-scale analysis across 158 MITx MOOC runs from 120 different courses offered on edX between 2013 and 2018, with 2.8 million enrollments. We see that learners from developing countries are less likely to complete MOOCs successfully, but we do not find evidence regarding a negative effect of not being English-native. Our findings point out that not only the specific population of learners is responsible for this bias, but also that the course itself has a similar impact. Independent of and less frequent than completion bias, we found assessment bias, that is when the mean ability gained by learners from developing countries is lower than that of learners from developed countries. The ability is inferred from the responses of the learners to the course-assessment using Item Response Theory (IRT).Finally, we applied Differential Item Functioning (DIF) methods with the objective of detecting items that might be causing the assessment bias, obtaining weak, yet positive results with respect to the magnitude of the bias reduction. Our results provide statistical evidence on the role that course design might have on these biases, with a call for action so that the future generation of MOOCs focus on strengthening their inclusive design approaches.

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Section: Course Design and Learning Achievementsmentioning

confidence: 96%

Section: Language Development Status and Learning In Moocsmentioning

confidence: 99%

Section: Inferring Demographics Traits Of Mooc Learnersmentioning

confidence: 99%

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

Gershon¹,

Ruipérez‐Valiente²,

Alexandron³

2021

Preprint

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“…A Further study by Uchidiuno et al [26] suggest that English language learners when compared with those for whom English is a first language show increased interaction with text content and less interaction with video and content without visual support.…”

mentioning

confidence: 97%

“…English Language Learners' (ELL) engagement on a MOOC which is delivered in English ( [24][25][26]).…”

mentioning

confidence: 99%

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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Interaction strategies in online learning: Insights from text analytics on iMOOC

Wang

Zhao

et al. 2022

Educ Inf Technol

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How Do English Language Learners Interact with Different Content Types in MOOC Videos?

Cited by 19 publications

References 26 publications

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

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

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

Interaction strategies in online learning: Insights from text analytics on iMOOC

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