Teaching the art of functional programming using automated grading (experience report)
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".