Treffer: Open badges and achievement goal orientation: a study with high-performing student programmers.

Earning Open Badges instead of regular grades and credits can be a motivating factor for high-performing students in terms of attending classes and completing assignments in extracurricular courses, but to what extent? And for what student profiles? To tackle these questions, we conducted a quantitative study with high-performing students. Each student involved in the study had consecutively attended two Java programming courses—one where credits and regular grades were issued for their achievements and performance in the course, and another extracurricular one where Open Badges were issued instead. The study compared the achievement goal orientation (AGO) of each student in the two courses (Wilcoxon paired test). It also examined how students' AGO scores in the Open-Badges-only course were associated with class attendance, completion of assignments and public display of their achievements (badges)—both as individual correlations with these variables (Spearman method), as well as associations with student profiles based on these variables (identified with Ward hierarchical clustering). The results indicate that high-performing students feel less motivated in terms of outperforming/under-performing others and have less fear of not learning enough if they receive Open Badges rather than regular grades. Also, a small portion of high-performers will be fully engaged in an Open-Badges-only course (attendance/completing assignments), while the majority will attend but complete a few assignments or just attend. Still, their AGO is not correlated with attending classes, completing assignments and displaying badges. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computing in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)