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To defend against collaborative cheating in code writing questions, instructors of courses with online, asynchronous exams can use the strategy of question variants. These question variants are manually written questions to be selected at random during exam time to assess the same learning goal. In order to create these variants, currently the instructors have to rely on intuition to accomplish the competing goals of ensuring that variants are different enough to defend against collaborative cheating, and yet similar enough where students are assessed fairly. In this paper, we propose data-driven investigation into these variants. We apply our data-driven investigation into a dataset of three midterm exams from a large introductory programming course. Our results show that (1) observable inequalities of student performance exist between variants and (2) these differences are not just limited to score. Our results also show that the information gathered from our data-driven investigation can be used to provide recommendations for improving design of future variants. 

We analyze the submissions of 286 students as they solved Structured Query Language (SQL) homework assignments for an upper-level databases course. Databases and the ability to query them are becoming increasingly essential for not only computer scientists but also business professionals, scientists, and anyone who needs to make data-driven decisions. Despite the increasing importance of SQL and databases, little research has documented student difficulties in learning SQL. We replicate and extend prior studies of students' difficulties with learning SQL. Students worked on and submitted their homework through an online learning management system with support for autograding of code. Students received immediate feedback on the correctness of their solutions and had approximately a week to finish writing eight to ten queries. We categorized student submissions by the type of error, or lack thereof, that students made, and whether the student was eventually able to construct a correct query. Like prior work, we find that the majority of student mistakes are syntax errors. In contrast with the conclusions of prior work, we find that some students are never able to resolve these syntax errors to create valid queries. Additionally, we find that students struggle the most when they need to write SQL queries related to GROUP BY and correlated subqueries. We suggest implications for instruction and future research.