@inproceedings{b41ae834087e4c97b0227abdfcae2924,
title = "A Quantitative Analysis of When Students Choose to Grade Questions on Computerized Exams with Multiple Attempts",
abstract = "In this paper, we study a computerized exam system that allows students to attempt the same question multiple times. This system permits students either to receive feedback on their submitted answer immediately or to defer the feedback and grade questions in bulk. An analysis of student behavior in three courses across two semesters found similar student behaviors across courses and student groups. We found that only a small minority of students used the deferred feedback option. A clustering analysis that considered both when students chose to receive feedback and either to immediately retry incorrect problems or to attempt other unfinished problems identified four main student strategies. These strategies were correlated to statistically significant differences in exam scores, but it was not clear if some strategies improved outcomes or if stronger students tended to prefer certain strategies.",
keywords = "agency, assessment, computer-based testing, computerized exams, multiple attempts",
author = "Ashank Verma and Timothy Bretl and Matthew West and Craig Zilles",
note = "Funding Information: This work was partially supported by NSF DUE-1915257 and the College of Engineering at the University of Illinois at Urbana-Champaign under the Strategic Instructional Initiatives Program (SIIP). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Publisher Copyright: {\textcopyright} 2020 ACM.; 7th Annual ACM Conference on Learning at Scale, L@S 2020 ; Conference date: 12-08-2020 Through 14-08-2020",
year = "2020",
month = aug,
day = "12",
doi = "10.1145/3386527.3406740",
language = "English (US)",
series = "L@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale",
publisher = "Association for Computing Machinery",
pages = "329--332",
booktitle = "L@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale",
}