Shifting the Load: a Peer Dialogue Agent that Encourages its Human Collaborator to Contribute More to Problem Solving

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• 作者：Cynthia Howard ; Pamela Jordan…
• 关键词：Peer agent ; Collaborative problem solving ; Collaborative dialogue ; Computer science education
• 刊名：International Journal of Artificial Intelligence in Education
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：27
• 期：1
• 页码：101-129
• 全文大小：
• 刊物类别：Artificial Intelligence (incl. Robotics); Educational Technology; User Interfaces and Human Computer
• 刊物主题：Artificial Intelligence (incl. Robotics); Educational Technology; User Interfaces and Human Computer Interaction; Computers and Education;
• 出版者：Springer New York
• ISSN：1560-4306
• 卷排序：27

文摘

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.