{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101112"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101112","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A multilevel logistic hidden Markov model for learning under cognitive diagnosis","abstract":"Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on preknowledge of how the learners’ and the instructional tools’ characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum, but also on the effectiveness of the particular learning tool and the its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.","abstract_html":"Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on preknowledge of how the learners’ and the instructional tools’ characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum, but also on the effectiveness of the particular learning tool and the its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.","abstract_has_math":false,"creators":["Zhang, Susu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Chang, Hua-Hua","Anderson, Carolyn J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:33:47Z","date_published":"2018-09-04T20:33:47Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Hidden Markov Model","Cognitive Diagnostic Modeling","Learning","Multilevel Modeling"],"languages":["en"],"rights":["Copyright 2018 Susu Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101112","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Hua-Hua","Anderson, Carolyn J."]},{"key":"dc:creator","label":"Author","values":["Zhang, Susu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:33:47Z","2020-09-05T09:15:26Z","2018-01-29","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"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":["Hidden Markov Model","Cognitive Diagnostic Modeling","Learning","Multilevel Modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Susu Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101112"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on preknowledge of how the learners’ and the instructional tools’ characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum, but also on the effectiveness of the particular learning tool and the its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Susu Zhang, accepted the attached license on 2018-01-25 at 15:42.","The student, Susu Zhang, submitted this Thesis for approval on 2018-01-25 at 15:48.","This Thesis was approved for publication on 2018-01-29 at 16:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12022 on 2018-08-31 at 17:17:03","Made available in DSpace on 2018-09-04T20:33:47Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2018.pdf: 1941838 bytes, checksum: 891edc91935443295ed00f781cf11d92 (MD5) LICENSE.txt: 4207 bytes, checksum: adfd9bfa335d4085fb66df63026bffc0 (MD5) Previous issue date: 2018-01-29","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:34:13Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107195 on 2020-09-05T09:15:26Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A multilevel logistic hidden Markov model for learning under cognitive diagnosis"]}]}],"canonical_facts":{"dc:contributor":["Chang, Hua-Hua","Anderson, Carolyn J."],"dc:creator":["Zhang, Susu"],"dc:date":["2018-09-04T20:33:47Z","2020-09-05T09:15:26Z","2018-01-29","2018-05"],"dc:description":["Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on preknowledge of how the learners’ and the instructional tools’ characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum, but also on the effectiveness of the particular learning tool and the its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Susu Zhang, accepted the attached license on 2018-01-25 at 15:42.","The student, Susu Zhang, submitted this Thesis for approval on 2018-01-25 at 15:48.","This Thesis was approved for publication on 2018-01-29 at 16:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12022 on 2018-08-31 at 17:17:03","Made available in DSpace on 2018-09-04T20:33:47Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2018.pdf: 1941838 bytes, checksum: 891edc91935443295ed00f781cf11d92 (MD5) LICENSE.txt: 4207 bytes, checksum: adfd9bfa335d4085fb66df63026bffc0 (MD5) Previous issue date: 2018-01-29","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:34:13Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107195 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107195 on 2020-09-05T09:15:26Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/101112"],"dc:language":["en"],"dc:rights":["Copyright 2018 Susu Zhang"],"dc:subject":["Hidden Markov Model","Cognitive Diagnostic Modeling","Learning","Multilevel Modeling"],"dc:title":["A multilevel logistic hidden Markov model for learning under cognitive diagnosis"],"dc:type":["text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:38Z"}