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  4. Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agents
 
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Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agents

Journal
Computer Human Interaction Conference (CHI)
Type
conference paper
Date Issued
2020
Author(s)
Winkler, Rainer  
;
Hobert, Sebastian
;
Salovaara, Antti
;
Söllner, Matthias  
;
Leimeister, Jan Marco  orcid-logo
Research Team
IWI6
Abstract
Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffolding learners’ understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.
Language
English
Keywords
Conversational agent
experiment
interactivity
online education
online videos
scaffolding
voice interaction
HSG Classification
contribution to scientific community
Event Title
Computer Human Interaction Conference (CHI)
Event Location
Honolulu, Hawaii
Event Date
25.04.2020 - 30.04.2020
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/113184
Subject(s)

information managemen...

Division(s)

IWI - Institute of In...

Eprints ID
261300
File(s)
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Thumbnail Image

open.access

Name

JML_796.pdf

Size

984.37 KB

Format

Adobe PDF

Checksum (MD5)

c367a9d33a9dd0641aabcac2eaadc01f

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