Show simple item record

dc.rights.licenseAll rights reserveden_US
dc.contributor.advisorDuffany, Jeffrey
dc.contributor.authorLedain Gentillon, Reginald
dc.date.accessioned2020-06-23T16:13:23Z
dc.date.available2020-06-23T16:13:23Z
dc.date.issued2019
dc.identifier.citationLedain Gentillon, R. (2019). At-Risk students prediction using machine learning [Unpublished manuscript]. Graduate School, Polytechnic University of Puerto Rico.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12475/178
dc.descriptionDesign Project Article for the Graduate Programs at Polytechnic University of Puerto Ricoen_US
dc.description.abstractThis article intends to discover how machine learning can be used to predict at-risk students during the school year. Different algorithms were tested within a common framework to compare their accuracy and their interpretability. Using some education expert knowledge, we examined each model relevance in relation to the most important features they used. Attendance, language proficiency and interim test completion were found to be very deterministic in the models prediction capabilities; not a surprise but a validation of the adequacy of the technology for this difficult task. Key Terms ⎯ Decision Trees, Deep Learning, Education, Machine Learning.en_US
dc.language.isoen_USen_US
dc.publisherPolytechnic University of Puerto Ricoen_US
dc.relation.ispartofComputer Science
dc.relation.ispartofseriesFall-2019
dc.relation.haspartSan Juan Campusen_US
dc.subject.lcshMachine learning
dc.subject.lcshPrediction of scholastic success
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Research
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Posters
dc.titleAt-Risk students prediction using machine learningen_US
dc.typeArticleen_US
dc.rights.holderPolytechnic University of Puerto Rico, Graduate Schoolen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record