TY - GEN
T1 - Learning to Give a Complete Argument with a Conversational Agent
T2 - 17th European Conference on Technology Enhanced Learning
AU - Mirzababaei, Behzad
AU - Pammer-Schindler, Viktoria
N1 - Funding Information:
Acknowledgements. This work was supported by the “DDAI” COMET Module within the COMET—Competence Centers for Excellent Technologies Program, funded by the Austrian Federal Ministry (BMK and BMDW), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Program is managed by FFG.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This paper reports a between-subjects experiment (treatment group N = 42, control group N = 53) evaluating the effect of a conversational agent that teaches users to give a complete argument. The agent analyses a given argument for whether it contains a claim, a warrant and evidence, which are understood to be essential elements in a good argument. The agent detects which of these elements is missing, and accordingly scaffolds the argument completion. The experiment includes a treatment task (Task 1) in which participants of the treatment group converse with the agent, and two assessment tasks (Tasks 2 and 3) in which both the treatment and the control group answer an argumentative question. We find that in Task 1, 36 out of 42 conversations with the agent are coherent. This indicates good interaction quality. We further find that in Tasks 2 and 3, the treatment group writes a significantly higher percentage of argumentative sentences (task 2: t(94) = 1.73, p = 0.042, task 3: t(94) = 1.7, p = 0.045). This shows that participants of the treatment group used the scaffold, taught by the agent in Task 1, outside the tutoring conversation (namely in the assessment Tasks 2 and 3) and across argumentation domains (Task 3 is in a different domain of argumentation than Tasks 1 and 2). The work complements existing research on adaptive and conversational support for teaching argumentation in essays.
AB - This paper reports a between-subjects experiment (treatment group N = 42, control group N = 53) evaluating the effect of a conversational agent that teaches users to give a complete argument. The agent analyses a given argument for whether it contains a claim, a warrant and evidence, which are understood to be essential elements in a good argument. The agent detects which of these elements is missing, and accordingly scaffolds the argument completion. The experiment includes a treatment task (Task 1) in which participants of the treatment group converse with the agent, and two assessment tasks (Tasks 2 and 3) in which both the treatment and the control group answer an argumentative question. We find that in Task 1, 36 out of 42 conversations with the agent are coherent. This indicates good interaction quality. We further find that in Tasks 2 and 3, the treatment group writes a significantly higher percentage of argumentative sentences (task 2: t(94) = 1.73, p = 0.042, task 3: t(94) = 1.7, p = 0.045). This shows that participants of the treatment group used the scaffold, taught by the agent in Task 1, outside the tutoring conversation (namely in the assessment Tasks 2 and 3) and across argumentation domains (Task 3 is in a different domain of argumentation than Tasks 1 and 2). The work complements existing research on adaptive and conversational support for teaching argumentation in essays.
KW - Argumentation
KW - Educational conversational agent
KW - Intelligent tutoring
KW - Toulmin’s model of argument
UR - http://www.scopus.com/inward/record.url?scp=85137975193&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16290-9_16
DO - 10.1007/978-3-031-16290-9_16
M3 - Conference paper
AN - SCOPUS:85137975193
SN - 9783031162893
T3 - Lecture Notes in Computer Science
SP - 215
EP - 228
BT - Educating for a New Future
A2 - Hilliger, Isabel
A2 - Muñoz-Merino, Pedro J.
A2 - De Laet, Tinne
A2 - Ortega-Arranz, Alejandro
A2 - Farrell, Tracie
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 September 2022 through 16 September 2022
ER -