{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:48:44Z","timestamp":1777870124799,"version":"3.51.4"},"reference-count":39,"publisher":"Sociedade Brasileira de Computacao - SB","issue":"1","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JBCS"],"abstract":"While higher education is the backbone for human capital development and economic growth, its high dropout rates remain a global concern that leads to wasted resources and unfulfilled student potential. Understanding dropout requires integrating social, economic, academic, and technical factors across students\u2019 trajectories, often interrelated in intricate, non-obvious ways. In this context, Process Mining (PM) offers a promising approach by uncovering patterns in students\u2019 interactions with academic programs and courses. However, traditional PM methods are typically established over mono perspectives of processes, which limits their ability to capture the multi-factor and correlated nature of educational trajectories. To address this gap, this paper proposes an extended PM-based approach that incorporates enriched labeling strategies that allow the simultaneous analysis of multiple dimensions of students' academic trajectories. Furthermore, the article presents a detailed application of the labeled method over real data of a Brazilian public university with 437,690 events from eight different programs, including students from the Unified Selection System (SISU). By comparing students' outcomes and paths, while considering their enrollment method, course option, and demographic information, we discovered that admission score, program, high school type, gender, and place of origin are the variables with a higher correlation to successful and less successful students. A deeper analysis of a specific program is also outlined to show how the approach can be customized for particular cases, under minor effort, while keeping standard input data.<\/jats:p>","DOI":"10.5753\/jbcs.2026.6559","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:24:53Z","timestamp":1777562693000},"page":"1090-1107","source":"Crossref","is-referenced-by-count":0,"title":["An Extended Process Mining Framework for the Multi-factor Analysis of Student Trajectories in Higher Education: The Dropout Problem"],"prefix":"10.5753","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2420-4094","authenticated-orcid":false,"given":"Luiz F. P.","family":"Southier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-7838","authenticated-orcid":false,"given":"Marcelo","family":"Teixeira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-8648","authenticated-orcid":false,"given":"Lovania R.","family":"Teixeira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3688-4066","authenticated-orcid":false,"given":"Sheila C.","family":"Freitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1117-7082","authenticated-orcid":false,"given":"Denise M. V.","family":"Sato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8530-6337","authenticated-orcid":false,"given":"Jair J.","family":"Ferronatto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3918-1799","authenticated-orcid":false,"given":"Edson E.","family":"Scalabrin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Araque, F., Rold\u00e1n, C., and Salguero, A. (2009). Factors influencing university drop out rates. Computers & Education<\/i>, 53(3):563-574. 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