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Auflistung nach Schlagwort "educational data mining"

1 - 3 von 3
  • Konferenzbeitrag
    A Decision Tree Approach for the Classification of Mistakes of Students Learning SQL, a case study about SELECT statements
    (DELFI 2020 – Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 2020) Faeskorn-Woyke, Heide; Bertelsmeier, Birgit; Strohschein, Jan
    TH Köln provides a web-based e-learning platform edb4, where novices can do their first steps in SQL. The goal of this paper is to build a decision tree (manually) that classifies the novice's errors. To do so we logged data containing tasks, solutions, and wrong statements over seven months and got a table with 7533 rows as a training set. Each leaf node of the decision tree is a class of errors of similar type and generates an error message with feedback to help the user to solve the task. Interesting and surprising are the mistakes that SQL novices make. The result improves the first steps of learning SQL in a simple and personalized way and gives the teachers hints to improve their learning outputs.
  • Konferenzbeitrag
    Enhancing educational insights: A real-time data analytics stack for project-basedlearning
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Gücük, Gian-Luca; Simic, Dejan; Leible, Stephan; Lewandowski, Tom; Kučević, Emir
    This paper presents a real-time data analytics (DA) stack designed for a project-based course utilizing Jira for project management at a university. The DA stack follows an Extract, Transform, and Load process to visualize students’ usage data within dashboards. The DA stack supports course management by providing insights into students’ activities and progress. We demonstrate the DA stack’s effectiveness through an evaluative case study, which was found to support course objectives and foster improved behavioral adaptations from lecturers to students. Furthermore, we propose a generic DA stack for generalizing and adopting it for similar applications, considering the extensibility and maintainability inherent in the open-source tools used. Moreover, we provide the GitHub repository to view our source code. This study contributes to the relatively underexplored field of real-time learning analytics and offers a starting point for the customization and adoption of the proposed DA stack in different educational contexts.
  • Konferenzbeitrag
    RAPP: A Responsible Academic Performance Prediction Tool for Decision-Making in Educational Institutes
    (BTW 2023, 2023) Duong, Manh Khoi; Dunkelau, Jannik; Cordova, José Andrés; Conrad, Stefan
    Due to the increasing importance of educational data mining for the early intervention of at-risk students and the growth of performance data collected in educational institutes, it becomes natural to employ machine learning models to predict student's performances based off prior data. Although machine learning pipelines are often similar, developing one for a specific target prediction of academic success can become a daunting task. In this work, we present a graphical user interface which implements a customisable machine learning pipeline which allows the training and evaluation of machine learning models for different definitions of academic success, \eg, collected credits, average grade, number of passed exams, etc. The evaluation is exported in PDF format after finishing training. As this tool serves as a decision support system for socially responsible AI systems, fairness notions were included in the evaluation to detect potential discrimination in the data and prediction space.