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Stationary vs. Non-stationary mobile learning in MOOCs. / Zhao, Yue; Robal, Tarmo; Lofi, Christoph; Hauff, Claudia.

UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. ed. / Jie Zhang; Tanja Mitrovic. NEW York, NY, USA : Association for Computing Machinery (ACM), 2018. p. 299-303.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Zhao, Y, Robal, T, Lofi, C & Hauff, C 2018, Stationary vs. Non-stationary mobile learning in MOOCs. in J Zhang & T Mitrovic (eds), UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), NEW York, NY, USA, pp. 299-303, 26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018, Singapore, Singapore, 8/07/18. https://doi.org/10.1145/3213586.3225241

APA

Zhao, Y., Robal, T., Lofi, C., & Hauff, C. (2018). Stationary vs. Non-stationary mobile learning in MOOCs. In J. Zhang, & T. Mitrovic (Eds.), UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 299-303). NEW York, NY, USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3213586.3225241

Vancouver

Zhao Y, Robal T, Lofi C, Hauff C. Stationary vs. Non-stationary mobile learning in MOOCs. In Zhang J, Mitrovic T, editors, UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. NEW York, NY, USA: Association for Computing Machinery (ACM). 2018. p. 299-303 https://doi.org/10.1145/3213586.3225241

Author

Zhao, Yue ; Robal, Tarmo ; Lofi, Christoph ; Hauff, Claudia. / Stationary vs. Non-stationary mobile learning in MOOCs. UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. editor / Jie Zhang ; Tanja Mitrovic. NEW York, NY, USA : Association for Computing Machinery (ACM), 2018. pp. 299-303

BibTeX

@inproceedings{677f95143fde46a0b0d22efeb3c439c8,
title = "Stationary vs. Non-stationary mobile learning in MOOCs",
abstract = "Mobile devices enable users to access information ubiquitously, including in the online learning scenario. This though requires users to multitask and divide their attention between several tasks at once whilst {"}on-the-go{"} (e.g. watching a video, walking down the street and keeping track of the traffic at the same time). In order to accommodate learners in this situation, most of today's Massive Open Online Course (MOOC) platforms provide mobile access to their content. Prior works have conducted lab studies to investigate the impact the learning condition (in particular stationary vs. onthe-go) has on mobile MOOC learners. User studies beyond the lab setting though are scarce. We here describe a study in a more realistic setup where 36 participants each participated in two mini-MOOCs while in a stationary and real-life mobile learning situation. We find participants' learning gains slightly lowered in the on-thego condition (-7{\%}).We also find that on average participants spend 10{\%} more time on video-watching and 23{\%} less time on questionanswering in the learning on-the-go compared to the stationary condition.",
keywords = "Divided Attention, Mobile Learning, MOOCs, User Study",
author = "Yue Zhao and Tarmo Robal and Christoph Lofi and Claudia Hauff",
year = "2018",
doi = "10.1145/3213586.3225241",
language = "English",
pages = "299--303",
editor = "Zhang, {Jie } and Mitrovic, {Tanja }",
booktitle = "UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Stationary vs. Non-stationary mobile learning in MOOCs

AU - Zhao, Yue

AU - Robal, Tarmo

AU - Lofi, Christoph

AU - Hauff, Claudia

PY - 2018

Y1 - 2018

N2 - Mobile devices enable users to access information ubiquitously, including in the online learning scenario. This though requires users to multitask and divide their attention between several tasks at once whilst "on-the-go" (e.g. watching a video, walking down the street and keeping track of the traffic at the same time). In order to accommodate learners in this situation, most of today's Massive Open Online Course (MOOC) platforms provide mobile access to their content. Prior works have conducted lab studies to investigate the impact the learning condition (in particular stationary vs. onthe-go) has on mobile MOOC learners. User studies beyond the lab setting though are scarce. We here describe a study in a more realistic setup where 36 participants each participated in two mini-MOOCs while in a stationary and real-life mobile learning situation. We find participants' learning gains slightly lowered in the on-thego condition (-7%).We also find that on average participants spend 10% more time on video-watching and 23% less time on questionanswering in the learning on-the-go compared to the stationary condition.

AB - Mobile devices enable users to access information ubiquitously, including in the online learning scenario. This though requires users to multitask and divide their attention between several tasks at once whilst "on-the-go" (e.g. watching a video, walking down the street and keeping track of the traffic at the same time). In order to accommodate learners in this situation, most of today's Massive Open Online Course (MOOC) platforms provide mobile access to their content. Prior works have conducted lab studies to investigate the impact the learning condition (in particular stationary vs. onthe-go) has on mobile MOOC learners. User studies beyond the lab setting though are scarce. We here describe a study in a more realistic setup where 36 participants each participated in two mini-MOOCs while in a stationary and real-life mobile learning situation. We find participants' learning gains slightly lowered in the on-thego condition (-7%).We also find that on average participants spend 10% more time on video-watching and 23% less time on questionanswering in the learning on-the-go compared to the stationary condition.

KW - Divided Attention

KW - Mobile Learning

KW - MOOCs

KW - User Study

UR - http://www.scopus.com/inward/record.url?scp=85051478414&partnerID=8YFLogxK

U2 - 10.1145/3213586.3225241

DO - 10.1145/3213586.3225241

M3 - Conference contribution

SP - 299

EP - 303

BT - UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

A2 - Zhang, Jie

A2 - Mitrovic, Tanja

PB - Association for Computing Machinery (ACM)

CY - NEW York, NY, USA

ER -

ID: 53163429