Design and implementation of a centralized course database for educational data integration
Sina, Nadia (2025)
Diplomityö
2025
School of Engineering Science, Tietotekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025073180509
https://urn.fi/URN:NBN:fi-fe2025073180509
Tiivistelmä
This thesis presents the design and development of a centralized database that integrates student data from different university platforms. Currently, student learning data such as data related to exams, assignments, and submissions is stored on different platforms. For example, LUT University uses tools like Moodle, Sisu, Exam, and CodeGrade (for programming courses) as the main platforms. This lack of centralization of data makes it difficult for teachers and researchers to get a full picture of student learning.
The solution proposed in this thesis is a centralized database that extracts all the data from different tools and gathers them into one place. The system uses a Python script to extract data from APIs and other resources and store the data in a MongoDB database. Also, it includes a dashboard in Power BI to help teachers and researchers view and analyze student performance.
The system was evaluated using real data from some programming courses and showed that it can manage large amounts of data, provide useful visualizations, and help track student learning behavior. This work can help improve teaching methods, support students more easily, and enable future research in learning analytics.
The solution proposed in this thesis is a centralized database that extracts all the data from different tools and gathers them into one place. The system uses a Python script to extract data from APIs and other resources and store the data in a MongoDB database. Also, it includes a dashboard in Power BI to help teachers and researchers view and analyze student performance.
The system was evaluated using real data from some programming courses and showed that it can manage large amounts of data, provide useful visualizations, and help track student learning behavior. This work can help improve teaching methods, support students more easily, and enable future research in learning analytics.