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Capturing and understanding learner competency development through interaction data offers the potential for early intervention and optimized educational design, yet introduces challenges related to scalability and complexity. We present\n                    <jats:styled-content>TracePath<\/jats:styled-content>\n                    , a novel graph\u2010based framework that models learner trajectories as directed graphs, where nodes correspond to competencies or learner states and edges denote transitions such as validation or rejection events. This approach uncovers common learning pathways, identifies bottlenecks, and supports predictive analytics. At the core, a generic metamodel formalizes Competency Transition Graphs (CTGs), enabling comprehensive graph\u2010based analytics implemented over a hybrid polystore architecture that integrates both relational and NoSQL databases. Our design decouples data extraction from graph exploration, allowing efficient querying, clustering, and pattern matching to deliver timely and explainable learning insights. Empirical validation using real\u2010world data from the\n                    <jats:styled-content>\u00e9cri+<\/jats:styled-content>\n                    e\u2010certification project demonstrates TracePath's effectiveness in providing scalable, dynamic, and low\u2010latency learning analytics to support personalized education.\n                  <\/jats:p>","DOI":"10.1002\/cpe.70508","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T02:57:08Z","timestamp":1767063428000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TracePath: Modeling and Analyzing Competency Trajectories With Graph\u2010Based Learning Analytics Over a Hybrid Polystore"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4257-0522","authenticated-orcid":false,"given":"Abdelkader","family":"Ouared","sequence":"first","affiliation":[{"name":"LIUM Computer Science Laboratory University of Le Mans  Le Mans France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madeth","family":"May","sequence":"additional","affiliation":[{"name":"LIUM Computer Science Laboratory University of Le Mans  Le Mans France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudine","family":"Piau\u2010Toffolon","sequence":"additional","affiliation":[{"name":"LIUM Computer Science Laboratory University of Le Mans  Le Mans France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Dugu\u00e9","sequence":"additional","affiliation":[{"name":"LIUM Computer Science Laboratory University of Le Mans  Le Mans France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"M.Crosslin \u201cCreating Online Learning Experiences: A Brief Guide to Online Courses From Small and Private to Massive and Open \u201d2018."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.23947\/2334-8496-2024-12-2-387-397"},{"key":"e_1_2_8_4_1","first-page":"1","article-title":"Using Ai\u2010Empowered Assessments and Personalized Recommendations to Promote Online Collaborative Learning Performance","volume":"57","author":"Zheng L.","year":"2024","journal-title":"Journal of Research on Technology in Education"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICALT.2015.128"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/03043797.2023.2219234"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e25383"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-023-11938-8"},{"key":"e_1_2_8_9_1","unstructured":"M.Stonebraker C.Bear U.\u00c7etintemel et al. \u201cOne Size Fits All? Part 2: Benchmarking Results \u201d2007."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2023.103044"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35329-1_11"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.59302\/y15yyn02"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41239-023-00423-4"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3424953.3426629"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.5753\/sbie.2021.218612"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11858-014-0598-7"},{"key":"e_1_2_8_17_1","unstructured":"J.Rogushina A.Gladun O.Anishchenko andS.Pryima \u201cSemantic Support of Personal Learning Trajectory Development \u201d2024."},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/educsci14070748"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/app15031426"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29526"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-020-10105-7"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-01-2024-0026"},{"issue":"1","key":"e_1_2_8_23_1","first-page":"64","article-title":"Integrating Knowledge Graphs and Causal Inference for AI\u2010Driven Personalized Learning in Education","volume":"1","author":"Liangkeyi S.","year":"2025","journal-title":"Artificial Intelligence Education Studies"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1108\/IJILT-07-2024-0162"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40594-025-00546-2"},{"issue":"1","key":"e_1_2_8_26_1","first-page":"51","article-title":"What is the Structure of a Challenge Based Learning Project? 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