{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:04:06Z","timestamp":1790787846750,"version":"4.1.0"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["ASTRA per ASPERA"],"award-info":[{"award-number":["ASTRA per ASPERA"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A significant amount of research has indicated that students\u2019 procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm\u2014using student\u2019s assignment submission behavior\u2014to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students\u2019 behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students\u2019 performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.<\/jats:p>","DOI":"10.3390\/e22010012","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T09:50:33Z","timestamp":1576835433000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["Mining Educational Data to Predict Students\u2019 Performance through Procrastination Behavior"],"prefix":"10.3390","volume":"22","author":[{"given":"Danial","family":"Hooshyar","sequence":"first","affiliation":[{"name":"Institute of Education, University of Tartu, Tartu 50103, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5087-9637","authenticated-orcid":false,"given":"Margus","family":"Pedaste","sequence":"additional","affiliation":[{"name":"Institute of Education, University of Tartu, Tartu 50103, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-7250","authenticated-orcid":false,"given":"Yeongwook","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Education, University of Tartu, Tartu 50103, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rovai, A.P., and Jordan, H. 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