{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RU7KTKDISFF3SRL7PPUC6G4K35","short_pith_number":"pith:RU7KTKDI","schema_version":"1.0","canonical_sha256":"8d3ea9a868914bb9457f7be82f1b8adf413a551633cb5cb8caa28752d2145aa4","source":{"kind":"arxiv","id":"2410.15054","version":1},"attestation_state":"computed","paper":{"title":"A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hong Qian, Jingwen Yang, Shuo Liu, Yimeng Liu, Yuanhao Liu","submitted_at":"2024-10-19T10:12:02Z","abstract_excerpt":"Cognitive diagnosis model (CDM) is a fundamental and upstream component in intelligent education. It aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because they can hardly directly infer new students' mastery levels or utilize new exercises or knowledge without retraining. Textual semantic information, due to its unified feature space and easy accessibility, can help alleviate this issue. Unf"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.15054","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-19T10:12:02Z","cross_cats_sorted":[],"title_canon_sha256":"b6f5f15d9fd3f6b17f70289eb5ab05b140fe435dbfede7195f23f7832edc46eb","abstract_canon_sha256":"b8300cdcf849aeef8e86d1d621ea33f3add308d226e39640cfeec2d618747138"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:49.477871Z","signature_b64":"fkjRC9XVVP5X9ugF0H2C+nBnzrud2FSzcy+X+uLsJjtY9bQUVSBMw0W1EoMpBka7mwi7LuG4et+NEh09jDnvDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d3ea9a868914bb9457f7be82f1b8adf413a551633cb5cb8caa28752d2145aa4","last_reissued_at":"2026-07-05T09:22:49.477375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:49.477375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hong Qian, Jingwen Yang, Shuo Liu, Yimeng Liu, Yuanhao Liu","submitted_at":"2024-10-19T10:12:02Z","abstract_excerpt":"Cognitive diagnosis model (CDM) is a fundamental and upstream component in intelligent education. It aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because they can hardly directly infer new students' mastery levels or utilize new exercises or knowledge without retraining. Textual semantic information, due to its unified feature space and easy accessibility, can help alleviate this issue. Unf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15054","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.15054/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.15054","created_at":"2026-07-05T09:22:49.477437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.15054v1","created_at":"2026-07-05T09:22:49.477437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15054","created_at":"2026-07-05T09:22:49.477437+00:00"},{"alias_kind":"pith_short_12","alias_value":"RU7KTKDISFF3","created_at":"2026-07-05T09:22:49.477437+00:00"},{"alias_kind":"pith_short_16","alias_value":"RU7KTKDISFF3SRL7","created_at":"2026-07-05T09:22:49.477437+00:00"},{"alias_kind":"pith_short_8","alias_value":"RU7KTKDI","created_at":"2026-07-05T09:22:49.477437+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21239","citing_title":"A Unified LLM-Adaptable Framework for Cold-Start Cognitive Diagnosis","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35","json":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35.json","graph_json":"https://pith.science/api/pith-number/RU7KTKDISFF3SRL7PPUC6G4K35/graph.json","events_json":"https://pith.science/api/pith-number/RU7KTKDISFF3SRL7PPUC6G4K35/events.json","paper":"https://pith.science/paper/RU7KTKDI"},"agent_actions":{"view_html":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35","download_json":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35.json","view_paper":"https://pith.science/paper/RU7KTKDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.15054&json=true","fetch_graph":"https://pith.science/api/pith-number/RU7KTKDISFF3SRL7PPUC6G4K35/graph.json","fetch_events":"https://pith.science/api/pith-number/RU7KTKDISFF3SRL7PPUC6G4K35/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35/action/storage_attestation","attest_author":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35/action/author_attestation","sign_citation":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35/action/citation_signature","submit_replication":"https://pith.science/pith/RU7KTKDISFF3SRL7PPUC6G4K35/action/replication_record"}},"created_at":"2026-07-05T09:22:49.477437+00:00","updated_at":"2026-07-05T09:22:49.477437+00:00"}