Designing Autograders for Novice Programmers

Hogg, Chad; Jump, Maria

doi:10.1145/3478432.3499147

Cited by 7 publications

(2 citation statements)

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“…of OJs are increasing in CS1 classes [1], [6], [7], due to their conveniences for both learners (e.g., automatic code correction) and instructors (e.g., workload reduction in providing feedback on students' code) [8], [9], [10], [11], [12]. Indeed, OJs facilitate the use of the Many Small Programs (MSP) approach [13], [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

et al. 2023

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Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, these topics were used to support programming competitions and experienced learners. However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.

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Section: Introductionmentioning

confidence: 99%

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

et al. 2023

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“…Online Judge (OJ) systems are a special kind of MOOC that provides a reliable, automatic, and instantaneous evaluation of an algorithm's source code sent by the learners [54,25,42,50,26,39,36]. The popularity of OJ systems are increasing in CS1 classes [54,20,37,11,57,49], due to their conveniences for learners (automatic code correction) and instructors (workload reduction in the students' code correction) [51,8,12,23,40,41,10]. Indeed, OJs facilitate the use of the Many Small Programs (MSP) approach [4,3,44,28], which requires the students to solve many smaller programming assignments weekly or biweekly, instead of a traditional One Larger Program (OLP), in which the learners solve a unique larger assignment in a short period of time.…”

Section: Introductionmentioning

confidence: 99%

Towards Supporting Cs1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Pereira¹,

Fonseca²,

Wiktor³

et al. 2022

Preprint

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<p>Online judges (OJs) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, the topics were used to support programming competitions and experienced learners. However, with OJs increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.</p>

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Ideation of Computational Thinking Programs by Assembling Code Snippets from the Web

Jamil

2023

Communications in Computer and Information Science

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Designing Autograders for Novice Programmers

Cited by 7 publications

References 0 publications

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Towards Supporting Cs1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Ideation of Computational Thinking Programs by Assembling Code Snippets from the Web

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