{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:26:03Z","timestamp":1785817563742,"version":"3.56.0"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students\u2019 classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students\u2019 learning behavioral patterns.<\/jats:p>","DOI":"10.3390\/info13010029","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T17:42:25Z","timestamp":1641836545000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students\u2019 Learning Behavior and Improving Teaching Quality"],"prefix":"10.3390","volume":"13","author":[{"given":"Hameed","family":"AlQaheri","sequence":"first","affiliation":[{"name":"College of Business Administration, Kuwait University, P.O. Box 5486, Safat 13055, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5713-9220","authenticated-orcid":false,"given":"Mrutyunjaya","family":"Panda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Applications, Utkal University, Vani Vihar, Bhubaneswar 751004, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15991","DOI":"10.1109\/ACCESS.2017.2654247","article-title":"A Systematic Review on Educational Data Mining","volume":"5","author":"Dutt","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.eswa.2006.04.005","article-title":"Educational data mining: A survey from 1995 to 2005","volume":"33","author":"Romero","year":"2007","journal-title":"Expert Syst. 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