CodeOcean - A versatile platform for practical programming excercises in online environments

Staubitz, Thomas; Klement, Hauke; Teusner, Ralf; Renz, Jan; Meinel, Christoph

doi:10.1109/educon.2016.7474573

Cited by 49 publications

(27 citation statements)

References 16 publications

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“…Many benefits of applying automatic assessment of programming assignments have been reported. Automatic assessment is more likely to be consistent and objective (Arifi, Abdellah, Zahi, & Benabbou, 2015;Staubitz, Klement, Teusner, Renz, & Meinel, 2016), enables rapid feedback (Arifi et al, 2015;Liu et al, 2016;Nordquist, 2007;Poon et al, 2018), and allows for students to submit multiple improved versions of the programs they have written (Del Fatto et al, 2017;English & English, 2015;Malmi, Korhonen, & Saikkonen, 2002;Pettit, Homer, Gee, Mengel, & Starbuck, 2015;Staubitz et al, 2016). Automatic assessment can play a motivational role in engaging students in the educational process (Šťastná, Juhár, Biňas, & Tomášek, 2015;Staubitz et al, 2016).…”

Section: Automatic Assessmentmentioning

confidence: 99%

Investigating the Feasibility of Automatic Assessment of Programming Tasks

Liebenberg

Pieterse

2018

JITE:IIP

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Aim/Purpose: The aims of this study were to investigate the feasibility of automatic assessment of programming tasks and to compare manual assessment with automatic assessment in terms of the effect of the different assessment methods on the marks of the students. Background: Manual assessment of programs written by students can be tedious. The assistance of automatic assessment methods might possibly assist in reducing the assessment burden, but there may be drawbacks diminishing the benefits of applying automatic assessment. The paper reports on the experience of a lecturer trying to introduce automated grading. Students’ solutions to a practical Java programming test were assessed both manually and automatically and the lecturer tied the experience to the unified theory of acceptance and use of technology (UTAUT). Methodology: The participants were 226 first-year students registered for a Java programming course. Of the tests the participants submitted, 214 were assessed both manually and automatically. Various statistical methods were used to compare the manual assessment of student’s solutions with the automatic assessment of the same solutions. A detailed investigation of reasons for differences was also carried out. A further data collection method was the lecturer’s reflection on the feasibility of automatic assessment of programming tasks based on the UTAUT. Contribution: This study enhances the knowledge regarding benefits and drawbacks of automatic assessment of students’ programming tasks. The research contributes to the UTAUT by applying it in a context where it has hardly been used. Furthermore, the study is a confirmation of previous work stating that automatic assessment may be less reliable for students with lower marks, but more trustworthy for the high achieving students. Findings: An automatic assessment tool verifying functional correctness might be feasible for assessment of programs written during practical lab sessions but could be less useful for practical tests and exams where functional, conceptual and structural correctness should be evaluated. In addition, the researchers found that automatic assessment seemed to be more suitable for assessing high achieving students. Recommendations for Practitioners: This paper makes it clear that lecturers should know what assessment goals they want to achieve. The appropriate method of assessment should be chosen wisely. In addition, practitioners should be aware of the drawbacks of automatic assessment before choosing it. Recommendation for Researchers: This work serves as an example of how researchers can apply the UTAUT theory when conducting qualitative research in different contexts. Impact on Society: The study would be of interest to lecturers considering automated assessment. The two assessments used in the study are typical of the way grading takes place in practice and may help lecturers understand what could happen if they switch from manual to automatic assessment. Future Research: Investigate the feasibility of automatic assessment of students’ programming tasks in a practical lab environment while accounting for structural, functional and conceptual assessment goals.

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Section: Automatic Assessmentmentioning

confidence: 99%

Investigating the Feasibility of Automatic Assessment of Programming Tasks

Liebenberg

Pieterse

2018

JITE:IIP

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“…Solutions for integrating MOOC platforms with hands-on training have been reported [13] [14]. Dakkak et al [13] proposed an online GPU development platform that provides students with a user-friendly scalable GPU computing platform.…”

Section: Related Workmentioning

confidence: 99%

“…Staubitz et al [14] introduced CodeOcean, a web platform to provide practical programming exercises for MOOCs. CodeOcean provides online courses with automated feedback and assessment of programming tasks.…”

Section: Related Workmentioning

confidence: 99%

An Open edX Extension for Parallel Programming Assignments with Automatic Configurable Grading

et al. 2021

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Computing devices of all types have almost converged to using central processing units featuring multiple processing cores. In order to develop efficient software for such devices, programmers need to learn how to write parallel programs. We present an infrastructure to support parallel programming assignments for online courses. We developed an extension to the Open edX platform with a backend that handles the execution of student codes on a cluster lab. The web user interface offers instructors a wide range of configuration options for the programming assignments as well as a flexible definition of criteria for automatic grading. We have successfully integrated the software with Open edX and tested it with a real parallel programming cluster lab.

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“…As described in the introduction, there is a certain demand to get advanced tools for storing and sharing some kinds of environment for data science and big data research, which is configured by specific engineering staff on cloud computing infrastructure for individuals and research teams. A lot of systems have been developed in recent years and solved some of the problems mentioned above, including big data environment infrastructure constructing [4,10,19], microservice-based platform [5,11,20], and online educational programming platform [21][22][23]. However, several issues remain, which are not addressed well by existing approaches and systems.…”

Section: Related Workmentioning

confidence: 99%

A Microservice-Based Big Data Analysis Platform for Online Educational Applications

Miao

Hong

et al. 2020

Scientific Programming

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The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

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CodeOcean - A versatile platform for practical programming excercises in online environments

Cited by 49 publications

References 16 publications

Investigating the Feasibility of Automatic Assessment of Programming Tasks

Investigating the Feasibility of Automatic Assessment of Programming Tasks

An Open edX Extension for Parallel Programming Assignments with Automatic Configurable Grading

A Microservice-Based Big Data Analysis Platform for Online Educational Applications

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