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Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time

Authors:

Frans Van der Sluis,

Tim Van der ZeeAuthors Info & Claims

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

Pages 51 - 60

https://doi.org/10.1145/2876034.2876051

Published: 25 April 2016 Publication History

Abstract

Understanding why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.

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Cited By

Alghamdi E(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
https://doi.org/10.1080/10494820.2024.2372833
Baneres DRodríguez-González MGuerrero-Roldán A(2023)A Real-Time Predictive Model for Identifying Course Dropout in Online Higher EducationIEEE Transactions on Learning Technologies10.1109/TLT.2023.326727516:4(484-499)Online publication date: 14-Apr-2023
https://dl.acm.org/doi/10.1109/TLT.2023.3267275
Yürüm OTaşkaya-Temizel TYıldırım S(2023)Predictive Video Analytics in Online Courses: A Systematic Literature ReviewTechnology, Knowledge and Learning10.1007/s10758-023-09697-z29:4(1907-1937)Online publication date: 4-Nov-2023
https://doi.org/10.1007/s10758-023-09697-z
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Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time

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Published In

cover image ACM Conferences

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

April 2016

446 pages

ISBN:9781450337267

DOI:10.1145/2876034

General Chair:
Jeff Haywood
The University of Edinburgh, UK
,
Program Chairs:
Vincent Aleven
Carnegie Mellon University, USA
,
Judy Kay
University of Sydney, Australia
,
Ido Roll
University of British Columbia, Canada

Copyright © 2016 ACM.

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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Published: 25 April 2016

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L@S 2016: Third (2016) ACM Conference on Learning @ Scale

April 25 - 26, 2016

Scotland, Edinburgh, UK

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L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

Alghamdi E(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
https://doi.org/10.1080/10494820.2024.2372833
Baneres DRodríguez-González MGuerrero-Roldán A(2023)A Real-Time Predictive Model for Identifying Course Dropout in Online Higher EducationIEEE Transactions on Learning Technologies10.1109/TLT.2023.326727516:4(484-499)Online publication date: 14-Apr-2023
https://dl.acm.org/doi/10.1109/TLT.2023.3267275
Yürüm OTaşkaya-Temizel TYıldırım S(2023)Predictive Video Analytics in Online Courses: A Systematic Literature ReviewTechnology, Knowledge and Learning10.1007/s10758-023-09697-z29:4(1907-1937)Online publication date: 4-Nov-2023
https://doi.org/10.1007/s10758-023-09697-z
van der Sluis Fvan den Broek E(2022)Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during readingJournal of the Association for Information Science and Technology10.1002/asi.2465774:1(3-16)Online publication date: 24-May-2022
https://doi.org/10.1002/asi.24657
Zhang FLiu DLiu C(2020)MOOC Video Personalized Classification Based on Cluster Analysis and Process MiningSustainability10.3390/su1207306612:7(3066)Online publication date: 10-Apr-2020
https://doi.org/10.3390/su12073066
Zhou YZhao JZhang J(2020)Prediction of learners’ dropout in E-learning based on the unusual behaviorsInteractive Learning Environments10.1080/10494820.2020.185778831:3(1796-1820)Online publication date: 23-Dec-2020
https://doi.org/10.1080/10494820.2020.1857788
Ashwin TGuddeti R(2020)Impact of inquiry interventions on students in e-learning and classroom environments using affective computing frameworkUser Modeling and User-Adapted Interaction10.1007/s11257-019-09254-3Online publication date: 4-Jan-2020
https://doi.org/10.1007/s11257-019-09254-3
Fassbinder MFassbinder AFioravanti MBarbosa EWeiss M(2019)Towards an educational design pattern language to support the development of open educational resources in videos for the MOOC contextProceedings of the 26th Conference on Pattern Languages of Programs10.5555/3492252.3492274(1-10)Online publication date: 7-Oct-2019
https://dl.acm.org/doi/10.5555/3492252.3492274
He HDong BZheng QLi GZhang MYang BCooper SLuxton-Reilly A(2019)VUCProceedings of the ACM Conference on Global Computing Education10.1145/3300115.3309514(99-105)Online publication date: 9-May-2019
https://dl.acm.org/doi/10.1145/3300115.3309514
Srivastava NVelloso ELodge JErfani SBailey JBrewster SFitzpatrick GCox AKostakos V(2019)Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-ReportProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300816(1-12)Online publication date: 2-May-2019
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