Instructional Science

, Volume 42, Issue 3, pp 327–351 | Cite as

Self-monitoring and knowledge-building in learning by teaching

  • Rod D. RoscoeEmail author


Prior research has established that learning by teaching depends upon peer tutors’ engagement in knowledge-building, in which tutors integrate their knowledge and generate new knowledge through reasoning. However, many tutors adopt a knowledge-telling bias defined by shallow summarizing of source materials and didactic lectures. Knowledge-telling contributes little to learning with deeper understanding. In this paper, we consider the self-monitoring hypothesis, which states that the knowledge-telling bias may arise due to tutors’ limited or inadequate evaluation of their own knowledge and understanding of the material. Tutors who fail to self-monitor may remain unaware of knowledge gaps or other confusions that could be repaired via knowledge-building. To test this hypothesis, sixty undergraduates were recruited to study and then teach a peer about a scientific topic. Data included tests of recall and comprehension, as well as extensive analyses of the explanations, questions, and self-monitoring that occurred during tutoring. Results show that tutors’ comprehension-monitoring and domain knowledge, along with pupils’ questions, were significant predictors of knowledge-building, which was in turn predictive of deeper understanding of the material. Moreover, tutorial interactions and questions appeared to naturally promote tutors’ self-monitoring. However, despite frequent comprehension-monitoring, many tutors still displayed a strong knowledge-telling bias. Thus, peer tutors appeared to experience more difficulty with self-regulatory aspects of knowledge-building (i.e., responding appropriately to perceived knowledge gaps and confusions) than with self-monitoring. Implications and alternative hypotheses for future research are discussed.


Learning by teaching Peer tutoring Metacognition Explaining Question-answering Expertise Personal epistemology 



The author would like to thank Michelene Chi, Kurt VanLehn, Chris Schunn, and Janet Schofield for their insights and expertise. The author is also grateful to Robert Hausmann, Marguerite Roy, Kirsten Butcher, Soniya Gadgil, and Bibinaz Pirayesh for their advice, suggestions, and assistance with many aspects of the research. This research was funded in part by Faculty of Arts and Sciences Summer Research Awards to the author from the University of Pittsburgh, and by a National Science Foundation award to the Pittsburgh Science of Learning Center (SBE-0354420) at Carnegie Mellon University and the University of Pittsburgh.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Learning Sciences InstituteArizona State UniversityTempeUSA

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