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Analytics projects for key stakeholders in large scale online learning systems

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As a computer-based learning platform, ASSISTments helped both educators and students across the country by providing a number of tools to aid in providing immediate feedback, to report meaningful data, and deliver instructional support. This has particularly been the case over the previous academic year where many schools were forced to shift to remote learning in response to the COVID-19 pandemic; during that time, over 20,000 teachers led more than 500,000 students to solve 30,000,000+ problems within the system. At this scale, it is imperative to not only support educators, but also provide the infrastructural support to developers, administrators, and other stakeholders who are involved in maintaining and improving ASSISTments. Within this, it is important to understand data needs for these different stakeholders, particularly as a data scientist in the team running the e-learning platform. There is copious amounts of student and teacher data related to homework assignments which has proved useful in studying aspects of learning, but this information also has the potential to positively affect day-to-day decision making for the stakeholders.In this thesis, three different levels of stakeholders are identified within existing data analytics projects, where the level of data granularity required for analysis increases with each level. These are the ASSISTments administrative team, teachers who interact with the system, and students who interact with the web-based tutor to complete assignments. This project seeks to focus on each of these three types of users to explore how the existing data within the platform may improve their differing experiences in support of improving the system.

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  • etd-27226
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  • 2021
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  • 2021-08-13
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Permanent link to this page: https://digital.wpi.edu/show/sx61dq42b