@inproceedings{1d244e7f12f143e5ac5db158e0099542,
title = "Evidence-Based Student Career and Performance Analysis with Process Mining: A Case Study",
abstract = "Educational process mining aims at leveraging data generated during students{\textquoteright} learning processes to extract evidence-based insights supporting the continuous improvements of educational programs. In this work, we showcase the application of educational process mining techniques to analyze students{\textquoteright} careers at an Italian university, focusing on their progression and outcomes. The study uncovers trends in compliance with curriculum requirements, exam-taking patterns and graduation times. Predictive models are then employed to elucidate the impact of different factors, e.g., the number of exams passed during the first year, on graduation times. These findings provide insights for educational institutions seeking support mechanisms to improve students{\textquoteright} success rates.",
keywords = "Curriculum Mining, Educational process mining, Student performance analysis",
author = "Domenico Potena and Laura Genga and Annalisa Basta and Chiara Mercati and Claudia Diamantini",
year = "2024",
month = apr,
day = "13",
doi = "10.1007/978-3-031-56107-8_27",
language = "English",
isbn = "978-3-031-56106-1",
series = "Lecture Notes in Business Information Processing (LNBIP)",
publisher = "Springer",
pages = "349--360",
editor = "{De Smedt}, Johannes and Pnina Soffer",
booktitle = "Process Mining Workshops",
address = "Germany",
note = "International workshops which were held in conjunction with 5th International Conference on Process Mining, ICPM 2023 ; Conference date: 23-10-2023 Through 27-10-2023",
}