Automated and Manual Grading of Web-Based Assignments

Peveler, Matthew; Maicus, Evan; Cutler, Barbara

doi:10.1145/3328778.3372682

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The WebWolf framework [214] allows instructors to write JUnit-like methods simulating user actions and to do assertions on the browser state to test websites, leveraging on Selenium automation framework to drive a headless web browser. Similarly, Peveler et al [179] presented a system which only difers from WebWolf in that it utilizes Docker and its concept of containers to run the student's code and required services (providing isolation) and allows the instructor to grade manually.…”

Section: Special Cs Domains (Rq1)mentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

113

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Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

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Section: Special Cs Domains (Rq1)mentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

113

View full text Add to dashboard Cite

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“…Due to security considerations [31,32], learners' programs are submitted to the server for execution and results retrieval. To prevent the submission of malicious programs or those with infinite loops that could potentially harm the server, we implemented Docker container technology [33]. Upon program submission, the system automatically creates a dedicated virtual container for execution.…”

Section: Securitymentioning

confidence: 99%

Design of an Online Programming Platform and a Study on Learners’ Testing Ability

Hsueh,

Lai,

Tseng

2023

Electronics

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Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners’ programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners’ programming abilities and proficiency by exploring students’ test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students’ learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at.

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Towards more Interactive Recordings for E-leaming Using Browser Automation

Carpio

2021

2021 IEEE Engineering International Research Conference (EIRCON)

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Automated and Manual Grading of Web-Based Assignments

Cited by 4 publications

References 2 publications

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Design of an Online Programming Platform and a Study on Learners’ Testing Ability

Towards more Interactive Recordings for E-leaming Using Browser Automation

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