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Characterizing ELL Students' Behavior During MOOC Videos Using Content Type

Published: 12 April 2017 Publication History

Abstract

Making MOOCs accessible to English Language Learners (ELLs) requires that students understand the language of instruction, and that instructional strategies address their unique learning challenges. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that ELL students exhibit distinct struggle behaviors in video portions without visual aids e.g., narrations without slides. Our findings challenge widely accepted multimedia design principles such as the split attention effect, provide insights into designing MOOC videos, and emphasize the need for adaptivity to increase MOOC access for ELLs.

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cover image ACM Conferences
L@S '17: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
April 2017
352 pages
ISBN:9781450344500
DOI:10.1145/3051457
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 April 2017

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Author Tags

  1. ell student identification
  2. english language learners
  3. language support interventions
  4. mooc behavioral analysis
  5. moocs

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  • Short-paper

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  • Google

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L@S 2017
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L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale
April 20 - 21, 2017
Massachusetts, Cambridge, USA

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L@S '17 Paper Acceptance Rate 14 of 105 submissions, 13%;
Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

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  • (2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
  • (2024)Millions of Views, But Does It Promote Learning? Analyzing Popular SciComm Production Styles Regarding Learning Success, User Behavior and PerceptionProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636861(134-144)Online publication date: 18-Mar-2024
  • (2024)Leveraging ensemble machine learning and multimodal video complexity for better prediction of video difficulty in second languageInteractive Learning Environments10.1080/10494820.2024.2372833(1-15)Online publication date: 29-Jul-2024
  • (2022)Using the YouTube Video Style in a MOOC: (Re-)Testing the Effect of Visual Experience in a Field-ExperimentProceedings of the Ninth ACM Conference on Learning @ Scale10.1145/3491140.3528268(142-150)Online publication date: 1-Jun-2022
  • (2021)What makes good LMOOCs for EFL learners? Learners’ personal characteristics and Information System Success ModelComputer Assisted Language Learning10.1080/09588221.2021.189924336:1-2(1-25)Online publication date: 23-Mar-2021
  • (2018)Scaling Classroom IT Skill TutoringProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174204(1-12)Online publication date: 21-Apr-2018
  • (2018)VUSphere: Visual Analysis of Video Utilization in Online Distance Education2018 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2018.8802383(25-35)Online publication date: Oct-2018

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