DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorSalmon, Ioannis-
dc.contributor.authorXanthopoulos, Theodoros-
dc.contributor.authorKytagias, Christos-
dc.contributor.authorPsaromiligkos, Ioannis (Yannis)-
dc.contributor.authorKytagias, Christos-
dc.contributor.authorGeorgakopoulos, Ioannis-
dc.date.accessioned2024-03-22T14:23:15Z-
dc.date.available2024-03-22T14:23:15Z-
dc.date.issued2020-06-01-
dc.identifierscopus-85086241762-
dc.identifier.isbn978-3-030-49663-0-
dc.identifier.issn1611-3349-
dc.identifier.issn0302-9743-
dc.identifier.other85086241762-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/1561-
dc.description.abstractInvestigating the factors affecting students’ academic failure in online and/or blended courses by analyzing students’ learning behavior data gathered from Learning Management Systems (LMS) is a challenging area in intelligent learning analytics and education data mining area. It has been argued that the actual course design and the instructor’s intentions is critical to determine which variables meaningfully represent student effort that should be included/excluded from the list of predicting factors. In this paper we describe such an approach for identifying students at risk of failure in online courses. For the proof of our concept we used the data of two cohorts of an online course implemented in Moodle LMS. Using the data of the first cohort we developed a prediction model by experimenting with certain base classifiers available in Weka. To improve the observed performance of the experimented base classifiers, we enhanced further our model with the Majority Voting ensemble classifier. The final model was used at the next cohort of students in order to identify those at risk of failure before the final exam. The prediction accuracy of the model was high which show that the findings of such a process can be generalized.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofIntelligent Tutoring Systems: Proceedings of the 16th International Conference, ITS 2020en_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.subjectIntelligent predictive analyticsen_US
dc.subjectLearning designen_US
dc.subjectMoodleen_US
dc.subjectStudents at risken_US
dc.titleIntelligent predictive analytics for identifying students at risk of failure in moodle coursesen_US
dc.typeConference Paperen_US
dc.relation.conference16th International Conference "Intelligent Tutoring Systems" (ITS 2020), 8-12 June 2020, Athens, Greeceen_US
dc.identifier.doi10.1007/978-3-030-49663-0_19en_US
dc.identifier.scopus2-s2.0-85086241762-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage152en_US
dc.identifier.epage162en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Paper-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.orcid0009-0006-9089-8898-
crisitem.author.orcid0000-0001-6037-0036-
crisitem.author.orcid0000-0002-8420-8663-
crisitem.author.orcid0000-0001-6037-0036-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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