{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97490"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97490","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Retain: building a concept recommendation system that leverages spaced repetition to improve retention in educational settings","abstract":"Made available in DSpace on 2017-08-10T19:16:13Z (GMT). No. of bitstreams: 2 SUBRAHMANYAM-THESIS-2017.pdf: 465119 bytes, checksum: fd9f648634920cc398844f5e0218f434 (MD5) LICENSE.txt: 4216 bytes, checksum: 956b25e149f76dafd42565463f11e7f3 (MD5) Previous issue date: 2017-04-26","abstract_html":"Made available in DSpace on 2017-08-10T19:16:13Z (GMT). No. of bitstreams: 2 SUBRAHMANYAM-THESIS-2017.pdf: 465119 bytes, checksum: fd9f648634920cc398844f5e0218f434 (MD5) LICENSE.txt: 4216 bytes, checksum: 956b25e149f76dafd42565463f11e7f3 (MD5) Previous issue date: 2017-04-26","abstract_has_math":false,"creators":["Subrahmanyam, Shilpa"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:16:13Z","date_published":"2017-08-10T19:16:13Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Spaced repetition","Education","Concept recommendation system"],"languages":["en"],"rights":["Copyright 2017 by Shilpa Subrahmanyam"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97490","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Subrahmanyam, Shilpa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:16:13Z","2017-04-26","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Spaced repetition","Education","Concept recommendation system"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 by Shilpa Subrahmanyam"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97490"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Made available in DSpace on 2017-08-10T19:16:13Z (GMT). No. of bitstreams: 2 SUBRAHMANYAM-THESIS-2017.pdf: 465119 bytes, checksum: fd9f648634920cc398844f5e0218f434 (MD5) LICENSE.txt: 4216 bytes, checksum: 956b25e149f76dafd42565463f11e7f3 (MD5) Previous issue date: 2017-04-26","There is a glaring lack of focus on long-term retention in today's educational paradigms. Moreover, research in the area of learning, memory, and specifically, promoting long-term retention has produced several robust and experimentally validated principles. A lot of this work can be leveraged to place some much-needed emphasis on long-term retention in educational settings. One such principle is spaced repetition -- a technique that has been empirically proven to promote long-term retention. The applications of current spaced repetition algorithms are limited to atomic concepts -- concepts that don't have any conceptual dependencies. In order to apply current spaced repetition formulae to more general contexts, we need to develop a system that can take conceptual dependencies into account. In this paper, we propose a framework called Retain that does exactly this. Retain is a system that can be used in virtually any educational context -- not just contexts that solely involve atomic concepts (i.e. learning vocabulary terms). It is a concept recommendation system that provides students with suggestions about when to review various concepts based on their understanding of parent concepts and the principle of spaced repetition. The results produced by Retain as well as the rules upon which Retain was built were evaluated by a group of teachers and were overwhelmingly favored over other concept recommendation baselines.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Shilpa Subrahmanyam, accepted the attached license on 2017-04-26 at 10:47.","The student, Shilpa Subrahmanyam, submitted this Thesis for approval on 2017-04-26 at 12:36.","This Thesis was approved for publication on 2017-04-26 at 17:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11083 on 2017-08-10 at 13:46:37"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Retain: building a concept recommendation system that leverages spaced repetition to improve retention in educational settings"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Subrahmanyam, Shilpa"],"dc:date":["2017-08-10T19:16:13Z","2017-04-26","2017-05"],"dc:description":["Made available in DSpace on 2017-08-10T19:16:13Z (GMT). No. of bitstreams: 2 SUBRAHMANYAM-THESIS-2017.pdf: 465119 bytes, checksum: fd9f648634920cc398844f5e0218f434 (MD5) LICENSE.txt: 4216 bytes, checksum: 956b25e149f76dafd42565463f11e7f3 (MD5) Previous issue date: 2017-04-26","There is a glaring lack of focus on long-term retention in today's educational paradigms. Moreover, research in the area of learning, memory, and specifically, promoting long-term retention has produced several robust and experimentally validated principles. A lot of this work can be leveraged to place some much-needed emphasis on long-term retention in educational settings. One such principle is spaced repetition -- a technique that has been empirically proven to promote long-term retention. The applications of current spaced repetition algorithms are limited to atomic concepts -- concepts that don't have any conceptual dependencies. In order to apply current spaced repetition formulae to more general contexts, we need to develop a system that can take conceptual dependencies into account. In this paper, we propose a framework called Retain that does exactly this. Retain is a system that can be used in virtually any educational context -- not just contexts that solely involve atomic concepts (i.e. learning vocabulary terms). It is a concept recommendation system that provides students with suggestions about when to review various concepts based on their understanding of parent concepts and the principle of spaced repetition. The results produced by Retain as well as the rules upon which Retain was built were evaluated by a group of teachers and were overwhelmingly favored over other concept recommendation baselines.","Submission original under an indefinite embargo labeled 'Open Access'. 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