Mohamed Elhayany
ABSTRACT
The popularity of Massive Open Online Courses (MOOCs) as a means of delivering education to large numbers of students has been growing steadily over the last decade. As technology improves, more educational content is becoming readily available to the public. JupyterLab, an open-source web-based interactive development environment (IDE), is also becoming increasingly popular in education, however, it is so far primarily used in small classroom settings. JupyterLab can provide a more interactive, hands-on, and collaborative learning experience for students in MOOCs, and it is highly customizable and can be accessed from anywhere. To capitalize on these benefits, we have developed OpenJupyter, which integrates JupyterLab at scale with MOOCs, enhancing the student learning experience and providing hands-on exercises for data science courses, making them more interactive and engaging. While MOOCs provide access to education for a large number of students, one of the significant challenges is providing effective and timely feedback to learners. OpenJupyter includes an auto-assessment capability that addresses this problem in MOOCs by automating the evaluation process and providing feedback to learners in a timely manner. In this paper, we provide an overview of the architecture of OpenJupyter, its scalability in the context of MOOCs, and its effectiveness in addressing the auto-assessment challenge. We also discuss the Advantages and limitations associated with using OpenJupyter in a MOOC context and provide a reference for educators and researchers who wish to implement similar tools. Our efforts aim to foster an open educational environment in the field of programming by providing learners with an interactive learning tool and a streamlined technical setup, allowing them to acquire and test their knowledge at their own pace.
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Index Terms
- Towards Automated Code Assessment with OpenJupyter in MOOCs
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