Toward instant gradeification

Zimmerman, Daniel M.; Kiniry, Joseph R.; Fairmichael, Fintan

doi:10.1109/cseet.2011.5876114

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Ala-Mutka [3], Douce [5], and Ihantola [7] all offer good reviews of the capabilities of tools that have come into existence since. Other relevant tools include ALOHA [1], Style++ [2], JUG [4], BlueJ [8], Autograder [9], ClockIt [10], Fitchfork [11], Mooshak [12], Bottlenose [13], AutoLep [14], and AutogradeMe [15]. We provide a more specific review of selected research below, as comparable examples of automated assessment tools (AATs) provide important context in considering our Athene AAT.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study of Iterative Improvement in Programming Assignments

Pettit

Homer

Gee

et al. 2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.

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Section: Related Workmentioning

confidence: 99%

An Empirical Study of Iterative Improvement in Programming Assignments

Pettit

Homer

Gee

et al. 2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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“…According to Douce et al , an automated assignment testing system was perhaps the earliest system developed by Hollingsworth in which students submit their assembly‐language‐written program using punch cards for AA. In the literature, researchers have discussed numerous automated assessment tools in their articles , for example, eGrader , CourseMarker , GAME , ASSYST , PASS , QUIMERA , Web‐Cat [11,12], BOSS , TRY , AutoLEP , PETCHA , Automata , EduPCR , Scheme‐robo , JavaMarker , Kassandra , SAC , Ceilidh , Athene , Mooshak , AutoGradeMe , SSBG , and much more.…”

Section: Related Workmentioning

confidence: 99%

“…AutoGradeMe is a tool to assess Java programs and relies on a set of plugin static‐checker software, where grade computations are configured separately, and a weighted average of outcomes is the result. The system provides continuous feedback during the development process.…”

Section: Automatic Analysis Approachesmentioning

confidence: 99%

The effect of automatic assessment on novice programming: Strengths and limitations of existing systems

Ullah

Lajis

Jamjoom

et al. 2018

Comp Applic In Engineering

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Computer programming is always of high concern for students in introductory programming courses. High rates of failure occur every semester due to lack of adequate skills in programming. No student can become a programmer overnight because such learning requires proper guidance as well as consistent practice with the programming exercises. The role of instructors in the development of students' learning skills is crucial in order to provide feedback on their errors and improve their knowledge accordingly. On the other hand, due to the large number of students, instructors are also overloading themselves to focus on each individual student's errors. To address these issues, researchers have developed numerous Automatic Assessment (AA) systems that not only evaluate the students' programs but also provide instant feedback on their errors as well as abridge the workload of the instructors. Due to the large pool of existing systems, it is difficult to cover each and every system in one study. Therefore, this paper provides a comprehensive overview of some of the existing systems based on the three‐analysis approaches: dynamic, static, and hybrid. Moreover, this paper aims to discuss the strengths and limitations of these systems and suggests some potential recommendations regarding the AA specifications for novice programming, which may help in standardizing these systems.

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“…In particular, the following metrics are reported: number of lines of code, number of tokens, number of comments, number of auxiliary functions, Haelstad's difficulty (DIF) and McCabe's cyclomatic complexity (CCN) 1 . Whereas these code metrics are generally despised as a measure for developer's productivity [31], they have been used in other automatic grading tools [20,27,35] and we think they are useful in the context of small, specific assignments as they provide a nice way to compare beginner solutions to expert solutions without disclosing the latter ones. Figure 3 shows a snapshot of the code metrics report on a (real) correct submission to the problem of returning the position of the first occurrence of an element in a sorted vector.…”

mentioning

confidence: 99%

<uri>Jutge.org</uri>: Characteristics and Experiences

Petit

Roura

Carmona

et al. 2018

IEEE Trans. Learning Technol.

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Jutge.org is an open educational online programming judge designed for students and instructors, featuring a repository of problems that is well organized by courses, topics and difficulty. Internally, Jutge.org uses a secure and efficient architecture and integrates modern verification techniques, formal methods, static code analysis and data mining. Jutge.org has exhaustively been used during the last decade at the Universitat Politècnica de Catalunya to strengthen the learning-by-doing approach in several courses. This paper presents the main characteristics of Jutge.org and shows its use and impact on a wide range of courses covering basic programming, data structures, algorithms, artificial intelligence, functional programming and circuit design.

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Toward instant gradeification

Cited by 6 publications

References 9 publications

An Empirical Study of Iterative Improvement in Programming Assignments

An Empirical Study of Iterative Improvement in Programming Assignments

The effect of automatic assessment on novice programming: Strengths and limitations of existing systems

<uri>Jutge.org</uri>: Characteristics and Experiences

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