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The purpose of this study was to investigate the collaborative dialogue patterns of pair programming and their impact on programming self‐efficacy and coding performance for both slow‐ and fast‐paced students. Forty‐six postgraduate students participated in the study. The students were asked to solve programming problems in pairs; those pairs' conversations were recorded when they worked on their tasks. Data analysis methods, including lag sequential analysis, cluster analysis and paired
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1 Faculty of Education, Shandong Normal University, Jinan, P.R. China
2 Digital Learning, California Lutheran University, Thousand Oaks, California, USA