Sud, P., & West, M., & Zilles, C. (2019, June), Reducing Difficulty Variance in Randomized Assessments  Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33228

\bibitem{asee_peer_33228}
  Sud, P., \& West, M., \& Zilles, C. (2019, June), \emph{Reducing Difficulty Variance in Randomized Assessments}
  Paper presented at 2019 ASEE Annual Conference \& Exposition , Tampa, Florida.
  10.18260/1-2--33228

Paras Sud, Matthew West, and Craig Zilles.  "Reducing Difficulty Variance in Randomized Assessments".  2019 ASEE Annual Conference & Exposition , Tampa, Florida, 2019, June.  ASEE Conferences, 2019.  https://strategy.asee.org/33228 Internet.  07 Jun, 2024

\bibitem{asee_peer_33228}
  Paras Sud, Matthew West, and Craig Zilles.
  "Reducing Difficulty Variance in Randomized Assessments".
  \emph{2019 ASEE Annual Conference \& Exposition , Tampa, Florida, 2019, June}.
  ASEE Conferences, 2019.
  https://strategy.asee.org/33228 Internet.  07 Jun, 2024

@INPROCEEDINGS{asee_peer_33228
author = "Paras Sud, Matthew West, and Craig Zilles"
title = "Reducing Difficulty Variance in Randomized Assessments"
booktitle = "2019 ASEE Annual Conference \& Exposition "
year = "2019"
month = "June"
address = "Tampa, Florida"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/33228}
number = {10.18260/1-2--33228}
}

TY  - CPAPER
AB  - When exams are run asynchronously (i.e., students take it at different times), a student can potentially gain an advantage by receiving information about the exam from someone who took it earlier. Generating random exams from pools of problems mitigates this potential advantage, but has the potential to introduce unfairness if the problems in a given pool are not identical difficulty. In this paper, we present an algorithm that takes a collection of problem pools and historical data on student performance on these problems and produces exams with reduced variance of difficulty (w.r.t. naive random selection) while maintaining sufficient variation between exams to ensure security. Specifically, for a synthetic example exam, we can roughly halve the standard deviation of generated assessment difficulty levels with negligible effects on cheating cost functions (e.g., entropy).
AU  - Paras Sud
AU  - Matthew West
AU  - Craig Zilles
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Reducing Difficulty Variance in Randomized Assessments
UR  - https://strategy.asee.org/33228
DO  - 10.18260/1-2--33228
ER  -