We propose a new collection of benchmark problems in mechanizing the metatheory of programming languages, in order to compare and push the state of the art of proof assistants. In particular, we focus on proofs using logical relations (LRs) and propose establishing strong normalization of a simply typed calculus with a proof by Kripke-style LRs as a benchmark. We give a modern view of this well-understood problem by formulating our LR on well-typed terms. Using this case study, we share some of the lessons learned tackling this problem in different dependently typed proof environments. In particular, we consider the mechanization in Beluga, a proof environment that supports higher-order abstract syntax encodings and contrast it to the development and strategies used in general-purpose proof assistants such as Coq and Agda. The goal of this paper is to engage the community in discussions on what support in proof environments is needed to truly bring mechanized metatheory to the masses and engage said community in the crafting of future benchmarks.

Online programming platforms have immense potential to improve students' educational experience. They make programming more accessible, as no installation is required; and automatic grading facilities provide students with immediate feedback on their code, allowing them to to fix bugs and address errors in their understanding right away. However, these graders tend to focus heavily on the functional correctness of a solution, neglecting other aspects of students' code and thereby causing students to miss out on a significant amount of valuable feedback.In this paper, we recount our experience in using the Learn-OCaml online programming platform to teach functional programming in a second-year university course on programming languages and paradigms. Moreover, we explore how to leverage Learn-OCaml's automated grading infrastructure to make it easy to write more expressive graders that give students feedback on properties of their code beyond simple input/output correctness, in order to effectively teach elements of functional programming style. In particular, we describe our extensions to the Learn-OCaml platform that evaluate students on test quality and code style.By providing these tools and a suite of our own homework problems and associated graders, we aim to promote functional programming education, enhance students' educational experience, and make teaching and learning typed functional programming more accessible to instructors and students alike, in our community and beyond.

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