The popularity of autograding has grown due to increasing class sizes and the need to reduce grading load while ensuring quality. Autograding has conventionally been used for multiple choice and fill in the blank questions, or to check code correctness. In this work, we discuss the use of autograders at UBC and some non-conventional autograding implementations in our curricula. We reflect upon our autograder use in our courses and discuss the benefits, implications, and considerations of this pedagogical choice.

Cold calling effectively incentivizes all students to actively prepare contributions to a class discussion, but some find it terrifying. Rewarding voluntarily speaking in class is less off-putting, and can be valuable for students who participate; however, it can allow a large fraction of the class to disengage. Agora is an open-source app designed to serve as a middle ground between these extremes, with the added benefit that it automatically produces an assessment of each student's engagement. The key ideas are to give students control over whether their hand is raised or lowered, to choose randomly among students with raised hands, and to give participation credit to all students who were considered every time a speaker is chosen. The system has various other features to facilitate deployment in large classes including multiple queues to support concurrent questions on different topics; a message board to allow students to communicate discretely with the instructor; and polling. We deployed the system in three offerings of a large undergraduate class and demonstrate its effectiveness in terms of learning outcomes, gender balance in participation, and student satisfaction.

Mechanical TA 2 (MTA2) is an open-source, distributed peer grading system that boosts performance by leveraging both trusted TAs and computationally intensive algorithms. The system provides a unified platform for submission of assignments, grading by both peers and TAs, and reporting of feedback. It also supports dividing students into different pools based on their peer-grading prowess; mechanisms for automated calibration and spot checking; and the ability for students to appeal grades and to give feedback about individual reviews. Bayesian inference and mixed-integer programming algorithms perform interpretable aggregation of peer grades and estimate students' grading performance, providing feedback, incentivizing high-quality grading, and directing TA spot checks appropriately. Analysis of data from four offerings of a large undergraduate class provides empirical evidence of MTA2's effectiveness.

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM), a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2

Peer grading systems aggregate noisy reports from multiple students to approximate a "true" grade as closely as possible. Most current systems either take the mean or median of reported grades; others aim to estimate students’ grading accuracy under a probabilistic model. This paper extends the state of the art in the latter approach in three key ways: (1) recognizing that students can behave strategically (e.g., reporting grades close to the class average without doing the work); (2) appropriately handling censored data that arises from discrete-valued grading rubrics; and (3) using mixed integer programming to improve the interpretability of the grades assigned to students. We demonstrate how to make Bayesian inference practical in this model and evaluate our approach on both synthetic and real-world data obtained by using our implemented system in four large classes. These extensive experiments show that grade aggregation using our model accurately estimates true grades, students' likelihood of submitting uninformative grades, and the variation in their inherent grading error; we also characterize our models' robustness.