Engaging Learners in Online Video Lectures with Dynamically Scaffolding Conversational Agents
Journal
European Conference on Information Systems (ECIS)
Type
conference paper
Date Issued
2020
Author(s)
;
Hobert, Sebastian
;
Fischer, Tizian
;
Salovaara, Antti
;
;
Research Team
IWI6
Abstract
Online education creates new opportunities for learners, which has led to sharply increasing enrollment in the last few years. Despite these benefits, past research shows that the lack of individual interaction with educators creates low learner engagement that leads to high attrition rates, which remains a major challenge in the field. Dynamically scaffolding conversational agents built into online video lectures promise to address this problem by individually interacting with learners, similar to educators’ scaffolding behavior. These agents are equipped with recent natural language processing capabilities, creating human-like conversations that help learners to be more engaged in the learning process. To test our hypothesis, we built a dynamically scaffolding conversational agent named Sara and compared it with an often-implemented static conversational agent built into two online video lectures. We deployed a lab experiment with 182 learners. The neurophysiological measurements revealed that Sara significantly improved learner engagement partly explained by differences in learners’ perceptions in the way they experienced the interaction. This study connects to already existing conversational agent studies in online education and highlights the importance of including dynamically scaffolding conversational agents in online video lectures to address the problem of low learner engagement in online education.
Language
English
Keywords
Conversational agent
learner engagement
dynamic scaffolding
NeuroIS experiment
HSG Classification
contribution to scientific community
Event Title
European Conference on Information Systems (ECIS)
Event Location
Marrakech, Morocco
Event Date
15.06.2020 - 17.06.2020
Official URL
Subject(s)
Division(s)
Eprints ID
261301
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open.access
Name
JML_791.pdf
Size
536.81 KB
Format
Adobe PDF
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