Scaling up functional programming education: under the hood of the OCaml MOOC

Canou, Benjamin; Cosmo, Roberto Di; Henry, Grégoire

doi:10.1145/3110248

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…In particular, tracking changes in a language can be useful in education [14,33,34]. Languages often develop quite quickly, new libraries and frameworks appear [20,41], and at the same time, new courses emerge for learning new technologies [37].…”

Section: Use Casesmentioning

confidence: 99%

Lupa: A Framework for Large Scale Analysis of the Programming Language Usage

Vlasova¹,

Maria²,

Власов³

et al. 2022

Preprint

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In this paper, we present Lupa -a platform for large-scale analysis of the programming language usage. Lupa is a command line tool that uses the power of the IntelliJ Platform under the hood, which gives it access to powerful static analysis tools used in modern IDEs. The tool supports custom analyzers that process the rich concrete syntax tree of the code and can calculate its various features: the presence of entities, their dependencies, definition-usage chains, etc. Currently, Lupa supports analyzing Python and Kotlin, but can be extended to other languages supported by IntelliJ-based IDEs. We explain the internals of the tool, show how it can be extended and customized, and describe an example analysis that we carried out with its help: analyzing the syntax of ranges in Kotlin.

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Section: Use Casesmentioning

confidence: 99%

Lupa: A Framework for Large Scale Analysis of the Programming Language Usage

Vlasova¹,

Maria²,

Власов³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This object is the continuation frame for f. We then immediately return and allow f's caller to do the same. (5) At the top of f, we add a block of code to check if the program is in restore mode, which indicates that a continuation is being applied. If so, we use the reified stack frame to restore local variables and the label saved on the stack.…”

Section: Our Approachmentioning

confidence: 99%

“…Web Workers Web Workers [79] are essentially isolated processes for JavaScript and a Web IDE can use them to terminate a user's program [5]. However, unlike Stopify, Workers do not provide richer execution control (e.g., pausing or breakpointing) or deep stacks.…”

Section: Related Workmentioning

confidence: 99%

Putting in all the stops: execution control for JavaScript

et al. 2018

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Scores of compilers produce JavaScript, enabling programmers to use many languages on the Web, reuse existing code, and even use Web IDEs. Unfortunately, most compilers inherit the browser's compromised execution model, so longrunning programs freeze the browser tab, infinite loops crash IDEs, and so on. The few compilers that avoid these problems suffer poor performance and are difficult to engineer. This paper presents Stopify, a source-to-source compiler that extends JavaScript with debugging abstractions and blocking operations, and easily integrates with existing compilers. We apply Stopify to ten programming languages and develop a Web IDE that supports stopping, single-stepping, breakpointing, and long-running computations. For nine languages, Stopify requires no or trivial compiler changes. For eight, our IDE is the first that provides these features. Two of our subject languages have compilers with similar features. Stopify's performance is competitive with these compilers and it makes them dramatically simpler.Stopify's abstractions rely on first-class continuations, which it provides by compiling JavaScript to JavaScript. We also identify sub-languages of JavaScript that compilers implicitly use, and exploit these to improve performance. Finally, Stopify needs to repeatedly interrupt and resume program execution. We use a sampling-based technique to estimate program speed that outperforms other systems.Preliminary Solutions There are a handful of robust programming language implementations for the Web: GopherJS (Go) [17], Pyret [57], Skulpt (Python) [68], Doppio (JVM) [77], GambitJS (Scheme) [71], and Whalesong (Racket) [83]. They use sophisticated compilers and runtime systems to support some subset of long-running computations, shared-memory concurrency, blocking I/O, proper tail calls, debugging, and

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“…In addition to the grade, we propose to consider additional factors such as the number of static errors and total time spent on solving programming assignments to identify student clusters. Using this methodology, we analyze the assignment submission data collected in a functional programming course taught at McGill university which uses the Learn-OCaml online programming platform [5,6,17]. This allows us to identify four student clusters: "Quick-learning", "Hardworking", "Satisficing", and "Struggling".…”

Section: Introductionmentioning

confidence: 99%

Identifying Different Student Clusters in Functional Programming Assignments: From Quick Learners to Struggling Students

Geng,

Xu,

et al. 2023

Preprint

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Instructors and students alike are often focused on the grade in programming assignments as a key measure of how well a student is mastering the material and whether a student is struggling. This can be, however, misleading. Especially when students have access to auto-graders, their grades may be heavily skewed.In this paper, we analyze student assignment submission data collected from a functional programming course taught at McGill university incorporating a wide range of features. In addition to the grade, we consider activity time data, time spent, and the number of static errors. This allows us to identify four clusters of students: "Quick-learning", "Hardworking", "Satisficing", and "Struggling" through cluster algorithms. We then analyze how work habits, working duration, the range of errors, and the ability to fix errors impact different clusters of students. This structured analysis provides valuable insights for instructors to actively help different types of students and emphasize different aspects of their overall course design. It also provides insights for students themselves to understand which aspects they still struggle with and allows them to seek clarification and adjust their work habits. CCS CONCEPTS• Social and professional topics → Student assessment.

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Scaling up functional programming education: under the hood of the OCaml MOOC

Cited by 11 publications

References 23 publications

Lupa: A Framework for Large Scale Analysis of the Programming Language Usage

Lupa: A Framework for Large Scale Analysis of the Programming Language Usage

Putting in all the stops: execution control for JavaScript

Identifying Different Student Clusters in Functional Programming Assignments: From Quick Learners to Struggling Students

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