Automatic assessment of OpenGL computer graphics assignments

Wünsche, Burkhard C.; Chen, Zhen; Shaw, Lindsay Alexander; Suselo, Thomas; Leung, Kai-Cheung; Dimalen, Davis; Mark, W. van der; Luxton-Reilly, Andrew; Lobb, Richard J.

doi:10.1145/3197091.3197112

Cited by 21 publications

(7 citation statements)

References 20 publications

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“…Very few papers discussed multiple tools, and for these papers, we annotated based on all the tools discussed, which were typically within the same domain as each other. Furthermore, few tools were discussed in multiple papers, with only Antlr [78,132,154,181,190], VPL [90,91,125], Coderunner [101,109,191] and Travis [88,122,123] appearing in three or more papers, with Antlr and Travis primarily being underlying technologies for AATs. While there is some duplication in terms of papers discussing multiple tools and tools being discussed in multiple papers, this duplication does not overly skew the results of the literature review.…”

Section: Resultsmentioning

confidence: 99%

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Messer,

Brown,

Kölling

et al. 2024

ACM Trans. Comput. Educ.

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We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques’ feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools’ performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

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Section: Resultsmentioning

confidence: 99%

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Messer,

Brown,

Kölling

et al. 2024

ACM Trans. Comput. Educ.

View full text Add to dashboard Cite

show abstract

“…This technique is only suitable for small coding assignments. Unit testing-based methods have been proposed for assessing graphical coding assignments (Wunsche et al, 2018), using pre-computed images as reference for comparison with images captured from the students' code. Assessing software correctness using tests assumes that the source code can be built and executed.…”

Section: Related Workmentioning

confidence: 99%

Automatic feedback and assessment of team-coding assignments in a DevOps context

Fernández-Gauna

Barona

Graña

2023

Int J Educ Technol High Educ

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We describe an automated assessment process for team-coding assignments based on DevOps best practices. This system and methodology includes the definition of Team Performance Metrics measuring properties of the software developed by each team, and their correct use of DevOps techniques. It tracks the progress on each of metric by each group. The methodology also defines Individual Performance Metrics to measure the impact of individual student contributions to increase in Team Performance Metrics. Periodically scheduled reports using these metrics provide students valuable feedback. This process also facilitates the process of assessing the assignments. Although this method is not intended to produce the final grade of each student, it provides very valuable information to the lecturers. We have used it as the main source of information for student and team assessment in one programming course. Additionally, we use other assessment methods to calculate the final grade: written conceptual tests to check their understanding of the development processes, and cross-evaluations. Qualitative evaluation of the students filling relevant questionnaires are very positive and encouraging.

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“…The focus is not on the submitted programs, but on whether the projects realised by learners contribute to develop their computational thinking skills. Another example, presented in [53,54], is the evaluation of OpenGL assignments, for which the goal is to check whether the submitted programs build the correct 3D scene. A last example is the integration of an automated code assessment feature in visualisation and simulation tools that are typically used for data structure and algorithm-related assignments [55,56].…”

Section: Other Aspectsmentioning

confidence: 99%

Automated Code Assessment for Education: Review, Classification and Perspectives on Techniques and Tools

Combéfis

2022

Software

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Automatically assessing code for learning purposes is a challenging goal to achieve. On-site courses and online ones developed for distance learning both require automated ways to grade learners’ programs to be able to scale and manage a large public with a limited teaching staff. This paper reviews recent automated code assessment systems. It proposes a systematic review of the possible analyses they can perform with the associated techniques, the kinds of produced feedback and the ways they are integrated in the learning process. It then discusses the key challenges for the development of new automated code assessment systems and the interaction with human grading. In conclusion, the paper draws several recommendations for new research directions and for possible improvements for automatic code assessment.

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Automatic assessment of OpenGL computer graphics assignments

Cited by 21 publications

References 20 publications

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Automatic feedback and assessment of team-coding assignments in a DevOps context

Automated Code Assessment for Education: Review, Classification and Perspectives on Techniques and Tools

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