Abstract
In higher education, Learning Management Systems (LMS) are widely used to enable a space where instructors can deliver the materials, propose and collect activities, and interact with students, among other functionalities. These LMSs allow collecting a vast amount of information whose analysis may improve the learning process and can even detect cheating situations. In the literature, there are some plugins to analyze LMS data, but the issue is that plugins need to be installed by the administrators. This demo presents Statoodle, a simple desktop tool aimed to analyze Moodle data based on exported logs, which can be obtained by any instructor. Among its functionalities, it can inspect activity within the exams to determine whether or not there are unauthorized accesses, which is something not often included in other plugins, it can provide detailed PDF reports about the overall performance of the quizzes (using algorithms, such as the Item Response Theory to analyze items’ difficulty), Excel reports with indicators about students’ activity and overall statistics about the grades. The tool has been tested in two courses, showing potential to detect exam fraud and/or prevent students from cheating when they are alerted that logs will be processed.
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Acknowledgement
This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the H2O Learn Project (grant PID2020-112584RB-C31), and the Madrid Regional Government through the e-Madrid-CM Project (Grant S2018/TCS-4307), which is co-funded by the European Structural Funds (FSE and FEDER).
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Moreno-Marcos, P.M., Barredo, J., Muñoz-Merino, P.J., Delgado Kloos, C. (2023). Statoodle: A Learning Analytics Tool to Analyze Moodle Students’ Actions and Prevent Cheating. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_70
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DOI: https://doi.org/10.1007/978-3-031-42682-7_70
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