Teaching the art of functional programming using automated grading
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 thesis, 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 present our extensions to the Learn-OCaml platform that evaluate students on test quality and code style.We then describe how we used these extensions in a later offering of our course to better pursue our teaching goals, and provide data from a study on students' code submissions to determine how students were using the platform.By providing our lessons learned over these semesters of using Learn-OCaml, as well as our new 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 all across the globe. AbrgLes plateformes de programmation en ligne ont un potentiel immense d'amliorer l'exprience ducationnelle des tudiants.Elles offrent une exprience de programmation plus accessible puisqu'elles ne requirent pas d'installation de configuration de la part des tudiants.De plus, les systmes de notation automatique permettent aux tudiants d'obtenir un retour d'information immdiat qui leur donne l'opportunit de corriger leurs erreurs et rectifier directement leur comprhension du problme.Cela dit, ces modules de notation ont tendance se concentrer sur l'exactitude du fonctionnement d'une solution aux dpends d'autres aspects du code.Cela rduit de faon significative la quantit d'information qui pourrait leur tre transmise.Dans ce mmoire, nous dcrivons notre exprience de l'utilisation en ligne de la plateforme Learn-OCaml pour enseigner des tudiants de deuxime anne la programmation fonctionnelle dans le cadre d'un cours sur les langages et les paradigmes de programmation.De plus, nous explorons de quelle faon nous pouvons utiliser l'infrastructure de notation automatique de Learn-OCaml pour faciliter l'criture de modules de notation plus expressifs.Ces modules, en plus de vrifier l'exactitude du code, permettent effectivement d'enseigner des lments de style de la programmation fonctionnelle.En particulier, nous prsentons nos extensions de la plateforme Learn-OCaml qui valuent les tudiants sur la qualit de leurs tests et le style de leur code.De plus, nous dcrivons comment nous avons utilis ces extensions dans le cadre d'un cours pour faciliter l'enseignement de bonnes pratiques de programmation et faisons l'analyse de donnes provenant d'une tude sur l'utilisation de la plateforme par les tudiants grce la soumission de leur code. travers la prsentation des leons tires de l'utilisation de la plateforme Learn-OCaml ainsi que des nouveaux outils et modules de notation que nous avons crs, nous dsirons faire la promotion de l'ducation de la programmation fonctionnelle, amliorer l'exprience ducationnelle des tudiants et rendre l'enseignement et l'apprentissage de la programmation fonctionnelle type plus accessible la fois aux enseignants et aux tudiants travers le monde.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".