In this study we explore how different methods of structuring collaborative interventions affect student learning and interaction in an Intelligent Tutoring System for Computer Science. We compare two methods of structuring collaboration: one condition, unstructured, does not provide students with feedback on their collaboration; whereas the other condition, semistructured, offers a visualization of group performance over time, partner contribution comparison and feedback, and general tips on collaboration. We present a contrastive analysis of student interaction outcomes between conditions, and explore students reported perceptions of both systems. We found that students in both conditions have significant learning gains, equivalent coding efficiency, and limited reliance on system examples. However, unstructured users are more on-topic in their conversational dialogue, whereas semistructured users exhibit better planning skills as problem difficulty increases.
[1]
Rachel Harsley.
Towards a Collaborative Intelligent Tutoring System Classification Scheme.
,
2014
.
[2]
Vincent Aleven,et al.
Adapting Collaboration Dialogue in Response to Intelligent Tutoring System Feedback
,
2015,
AIED.
[3]
Kenneth R. Koedinger,et al.
Integrating Collaboration and Intelligent Tutoring Data in the Evaluation of a Reciprocal Peer Tutoring Environment
,
2009,
Res. Pract. Technol. Enhanc. Learn..
[4]
Anastasios Karakostas,et al.
Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field
,
2011,
IEEE Transactions on Learning Technologies.