Impact of Both Prior Knowledge and Acquaintanceship on Collaboration and Performance: A Pair Program Tracing and Debugging Eye-Tracking Experiment

• Main Authors: Villamor, Maureen, Rodrigo, Ma. Mercedes T.
• Format: text
• Published: Archīum Ateneo 2017
• Subjects: Eye-tracking; collaboration; cross-recurrence quantification analysis; Computer Sciences; Databases and Information Systems
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/320; https://www.apsce.net/icce/icce2017/140.115.135.84/icce/icce2017/proceedings_main.html
• Institution: Ateneo De Manila University

We compared the collaboration of pairs whose composition was based on both prior knowledge and degree of acquaintanceship as they traced and debugged fragments of code. We performed a cross-recurrence quantification analysis (CRQA) to build cross-recurrence plots using the eye tracking data and computed for the CRQA metrics, such as recurrence rate (RR), determinism (DET), entropy (ENTR), and laminarity (LAM) using the CRP toolbox for MATLAB. Findings revealed that high prior knowledge pairs who were poorly acquainted (BH/PA) performed better among categories despite having collaborated the least. This confirmed the findings of prior studies that skilled strangers perform best. Mixed prior knowledge pairs who were highly acquainted (M/HA) collaborated the most but their familiarity did not translate to better performance. The results of this study could contribute to the learning sciences and pedagogy. If we know what makes collaboration successful as measured through their performance, we can design interventions that could facilitate the process of creating programming pairs who can collaborate and perform better.