This study investigates the consistency and fairness of grading in higher education by analyzing the impact of individual graders on class averages, identifying grading inconsistencies through similarity assessments of essays, and examining the correlation between the use of detailed grading rubrics and the frequency of student re-grade requests. Utilizing a comprehensive dataset of student essays, assigned grades, and grading rubrics, the research employs statistical analyses and natural language processing techniques to address these objectives at scale. Using the central limit theorem, we can identify significant differences in overall scoring between graders. Additionally, the study reveals instances where textually similar essays received markedly different grades, highlighting potential inconsistencies in grading practices. Furthermore, our analysis reveals that graders who apply a larger number of rubric items in their grading tend to receive higher number of student regrade requests (r>0.87). This indicates that while rubric-based grading is intended to enhance transparency, its extensive use may leave room for subjective interpretation, leading to more frequent grade challenges. These insights contribute to the ongoing discourse on improving grading practices and offer recommendations for enhancing transparency and equity in academic assessments at scale.

Examinator v3.0 detects cheating in online take-home exams by comparing answers and the timestamps they were entered. A web interface enables efficient manual inspection. Use of the tool reveals that certain question types substantially enhance cheating detection, demonstrating the potential of automated algorithmic detection at scale. Examinator v3.0 has analyzed 915,831 pairs of exam submissions across three courses over two semesters at a top U.S. institution, identifying 46 instances of cheating.

Plagiarism in computer science programs remains a problem at many universities. Programming assignments lend themselves to unpermitted collaboration: students can share code or find solutions from past semesters through simple internet searches. JackMarker helps address this problem by embedding a hidden, traceable, and unique token within documents. GitDown extends this work by locating available online solutions and requesting that they be removed.

Examinator compares pairs of take-home exams to select which should be manually checked for plagiarism. Examinator also generates a report with evidence for these cases using its metrics and those generated as a by-product of the commercial grading tool Gradescope. Examinator supports degree-seeking graduate programs (both online and on-campus) at a top computer science graduate institute in the United States. Since Spring 2019, Examinator has compared over 2 million pairs of exams from a popular Artificial Intelligence course, resulting in 56 cases being referred for discipline. Iterative development has improved the percentage of referrals of suggested cases from 15% to 25%.

As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.

Cheating has always been a problem for academic institutions, but the internet has increased access to a form of academic dishonesty known as contract cheating, or "homework for hire." When students purchase work online and submit it as their own, it cannot be detected by commonly-used plagiarism detection tools, and this troubling form of cheating seems to be increasing.