Shifting the Load: a Peer Dialogue Agent that Encourages its Human Collaborator to Contribute More to Problem Solving
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Shifting the Load: a Peer Dialogue Agent that Encourages its Human Collaborator to Contribute More to Problem Solving Cynthia Howard1 · Pamela Jordan2 · Barbara Di Eugenio3 · Sandra Katz2
© International Artificial Intelligence in Education Society 2015
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.
Pamela Jordan
[email protected] Cynthia Howard [email protected] Barbara Di Eugenio [email protected] Sandra Katz [email protected] 1
Computer and Mathematical Sciences Department, Lewis University, One University Parkway, Romeoville, IL 60446-2200, USA
2
Learning Research and Development Center, University of Pittsburgh, 3939 O’Hara St, Pittsburgh, PA 15260, USA
3
Department of Computer Science, University of Illinois at Chicago, 1120 Science & Engineering Offices (MC 152) 851 South Morgan, Chicago, IL 60607, USA
Int J Artif Intell Educ
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. Keywords Peer agent · Collaborative problem solving · Collaborative dialogue · Computer science education
Introduction Introductory data structures and their related algorithms is one of the core components of Computer Science education. A deep understanding of this topic is essential to a strong Computer Science foundation, as attested by the curricula promoted by national and international professional societies (AA.VV. 2001, 2007, 2013; Scime 2008). Computer Science is of enormous strategic interest, and it is projected to foster vast job growth in the next few years (AA. VV. 2014; Brienza 2012); however, in the United States there is a dearth of qualified professionals to step into these openings.
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