User anonymity versus identification in computer-supported collaborative learning: comparing learners' preferences and behaviors

Velamazán, Mariano; Santos, Patricia; Hernández-Leo, Davinia; Vicent, Lluís

doi:http://dx.doi.org/10.1016/j.compedu.2023.104848

User anonymity versus identification in computer-supported collaborative learning: comparing learners' preferences and behaviors

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Velamazán M, Santos P, Hernández-Leo D, Vicent L. User anonymity versus identification in computer-supported collaborative learning: comparing learners' preferences and behaviors. Comput Educ. 2023 Sep;203:104848. DOI: 10.1016/j.compedu.2023.104848

Abstract

Previous research on the effect of anonymity on computer-supported collaborative learning has reported mostly positive results, as well as some negative results. Scholars have indicated that anonymity can promote more participation among members; however, it can also promote off-task behavior and spam. Although the concept has been investigated in different contexts (peer assessment, writing, debating, etc.) and from different perspectives (for example, social psychology, computer mediated communication), students' preferences and whether these preferences align with students' actual behavior during collaborative learning remain uncertain. In this study, we compared students' opinions with their technology-mediated conversations when they collaborated while anonymized versus while identified. Our results were derived from a survey and content analysis of conversation logs that included 186 university students. These findings suggest that anonymity promotes a less productive atmosphere, though students seem to prefer it. We present our findings about how anonymity affects the quantity and quality of collaboration, a group's work balance, and spam messages' role, among other factors. Moreover, we contribute new knowledge about the social component of the social identity model of deindividuation effects (SIDE). Finally, we propose new features for computer-supported collaborative learning tools that could help optimize anonymity to maximize its potential benefits.