Shifting the Load: A Peer Dialogue Agent That Encourages Its Human Collaborator to Contribute More to Problem Solving

Autor/inn/en	Howard, Cynthia; Jordan, Pamela; Di Eugenio, Barbara; Katz, Sandra
Titel	Shifting the Load: A Peer Dialogue Agent That Encourages Its Human Collaborator to Contribute More to Problem Solving
Quelle	In: International Journal of Artificial Intelligence in Education, 27 (2017) 1, S.101-129 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-015-0071-y
Schlagwörter	Computer Science Education; Undergraduate Study; Intelligent Tutoring Systems; Artificial Intelligence; Problem Solving; Cooperative Learning; Student Behavior; Behavior Modification; Interaction + Suchen Sie Ihr Suchwort? Computer science lessons; Informatikunterricht; Grundstudium; Intelligentes Tutorsystem; Künstliche Intelligenz; Problemlösen; Kooperatives Lernen; Student behaviour; Schülerverhalten; Behaviour modification; Verhaltensänderung; Interaktion
Abstract	Despite a growing need for educational tools that support students at the earliest phases of undergraduate Computer Science (CS) curricula, relatively few such tools exist--the majority being Intelligent Tutoring Systems. Since peer interactions more readily give rise to challenges and negotiations, another way in which students can become more interactive during problem solving, we created an artificial peer collaborator to determine its value for aiding CS students. Central to its development was the notion that it should monitor the student's collaborative behavior and attempt to guide him/her towards more productive behavior. In prior work, we found that "initiative shifts" correlate with both Knowledge Co-Construction (KCC) and learning and are potentially easier to model as an indicator of productive collaboration in instructional software. In this paper, we describe a unique peer dialogue agent that we created to test the effects of tracking and reacting to initiative shifts. While our study did not find differences in learning gains when comparing agents that do and do not track and react to initiative shifts, we did find that students do learn when interacting with the agent and that attempting to influence initiative taking did make a difference. This suggests that by tracking initiative shifts, the agent was able to detect times when the student had been letting the agent do most of the "deep thinking" and that the agent's tactics for encouraging the student to begin taking the initiative again were helpful. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01