The boss online submission and assessment system

Joy, Mike; Griffiths, Nathan; Boyatt, Russell

doi:10.1145/1163405.1163407

Cited by 224 publications

(149 citation statements)

References 19 publications

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“… Hybrid Approach: These tools incorporatestrong points of both the abovementioned approaches. Under this approach, three different strategies were identified: -Preliminary validation [5]: Instructor starts the assessment process, but a preliminary evaluation of the solution is carried out by the tool.BOSS [9], OTO [30] and PASS [7] are few tools that implement this strategy. [5]: The assessment tool is used to evaluate codes of all students using specific test cases.…”

Section: Tools Reviewmentioning

confidence: 99%

“…The accessibility to automated tools [8,9] to assist the evaluation of students' work and providing students with appropriate and timely feedback can really help in motivating the student to learn with more focus [1,24]. The feedback provided by the tool can also be further enhanced by using some techniques to direct a student to engage in self learning aiming at improvement Although, building such tools for evaluating students' work for all kind of courses is not yet feasible.…”

Section: Introductionmentioning

confidence: 99%

“…Also, students are motivated to constantly improvise their assignments by re-submissions until an appropriate solution is obtained [4,9].…”

Section: Introductionmentioning

confidence: 99%

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E-Assessment Tools for Programming Languages: A Review

Gupta¹,

Gupta²

2018

Annals of Computer Science and Information Systems

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Abstract-Continuous Evaluation and feedback not only helps in improving learning of a student, but also acts as a constant motivator to put in more efforts. But then, feedback and assessment are very difficult and time consuming in practice. Thus, automating the entire system of assessment, evaluation and feedback will be highly beneficial. But, building such tools for all courses is yet not feasible. However, e-assessment tools for programming courses in Computer Science discipline can be developed. In this paper, we review various grading techniques used by these tools to assess a student's programming assignment. Further, this paper discusses various types and features of tools according to which an appropriate tool should be selected. And, in the end, we will be highlighting the extent to which students and instructors are actually benefited by these tools.

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Section: Tools Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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E-Assessment Tools for Programming Languages: A Review

Gupta¹,

Gupta²

2018

Annals of Computer Science and Information Systems

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“…Different similarity measurements such as suffix trees, string alignment, Jaccard similarity, etc., can be applied to sequences or sets of tokens. Tools that rely on tokens include Sherlock (Joy and Luck 1999), BOSS (Joy et al 2005), Sim (Gitchell and Tran 1999), YAP3 (Wise 1996), JPlag (Prechelt et al 2002), CCFinder (Kamiya et al 2002), CP-Miner (Li et al 2006), MOSS (Schleimer et al 2003), Burrows et al (2007), and the Source Code Similarity Detector System (SCSDS) (Duric and Gasevic 2013). The token-based representation is widely used in source code similarity measurement and very efficient on a scale of millions SLOC.…”

Section: Code Similarity Measurementmentioning

confidence: 99%

A comparison of code similarity analysers

2017

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Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

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“…These systems are more likely to be used in practice because they have fewer limitations and are not as rigid as some automated approaches to programming assessment. An example of one semi-automated system is the BOSS system for electronic assessment of Java programming code (Joy, Griffiths & Boyatt, 2005). This system operates by running the students code through pre-specified test cases and automatically assigning marks based on these results.…”

Section: 1mentioning

confidence: 99%

Investigating shareable feedback tags for programming assignments

Cummins

Burd

Hatch

2011

Computer Science Education

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Investigating sharable feedback tags for programming assignmentsThis paper presents an investigation into the usage of sharable feedback tags as a way of delivering feedback to three different cohorts of programming students. A series of research questions are investigated which include investigating any perceived benefit from students using feedback tags and investigating how students interact with their feedback. Results indicate that students with both the lower and higher marks in a cohort are more likely to opt to share their feedback and programming work than students with mid-ranged marks. A variety of reasons for and against sharing given by students are discussed. Six categories of student behaviour exhibited during interaction with their feedback have been identified in this paper. This paper has shown that feedback tags can be used successfully as a form of sharable feedback and that a number of future research possibilities exist that can extend this topic.

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The boss online submission and assessment system

Cited by 224 publications

References 19 publications

E-Assessment Tools for Programming Languages: A Review

E-Assessment Tools for Programming Languages: A Review

A comparison of code similarity analysers

Investigating shareable feedback tags for programming assignments

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