{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LSTDPP2JI2KU4R5ESSZ3NC4PR4","short_pith_number":"pith:LSTDPP2J","schema_version":"1.0","canonical_sha256":"5ca637bf4946954e47a494b3b68b8f8f0649a22698bc0766610b723ddb294d84","source":{"kind":"arxiv","id":"2407.05458","version":1},"attestation_state":"computed","paper":{"title":"A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Enhong Chen, Fei Wang, Guanhao Zhao, Jiatong Li, Mengxiao Zhu, Qi Liu, Shijin Wang, WeiBo Gao, Wei Tong, Zheng Zhang, Zhenya Huang","submitted_at":"2024-07-07T18:02:00Z","abstract_excerpt":"Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery. It has been applied to a wide range of fields including education, sport, psychological diagnosis, etc. By providing better awareness of cognitive status, it can serve as the basis for personalized services such as well-designed medical treatment, teaching strategy and vocational training. This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-bas"},"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":"2407.05458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-07T18:02:00Z","cross_cats_sorted":[],"title_canon_sha256":"e5ce43309c7190487edc025e2781fe286d41f532d295c26c72ae955bcea6e4e6","abstract_canon_sha256":"413cc8ca61bd3d6ddc5423c7e850d7d53feab8f18d000b5d31505f9e3df218b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:13.580131Z","signature_b64":"7qgRjOv7Ijpz3kCleYyIPjUUfO7qune2yLxdX7gFfJCZrE4EEzliFBNHqjowKzv+TQ3OE1Ltb/PTGCMAfHSTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ca637bf4946954e47a494b3b68b8f8f0649a22698bc0766610b723ddb294d84","last_reissued_at":"2026-07-05T08:41:13.579701Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:13.579701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Enhong Chen, Fei Wang, Guanhao Zhao, Jiatong Li, Mengxiao Zhu, Qi Liu, Shijin Wang, WeiBo Gao, Wei Tong, Zheng Zhang, Zhenya Huang","submitted_at":"2024-07-07T18:02:00Z","abstract_excerpt":"Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery. It has been applied to a wide range of fields including education, sport, psychological diagnosis, etc. By providing better awareness of cognitive status, it can serve as the basis for personalized services such as well-designed medical treatment, teaching strategy and vocational training. This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05458","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/2407.05458/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":"2407.05458","created_at":"2026-07-05T08:41:13.579761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.05458v1","created_at":"2026-07-05T08:41:13.579761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05458","created_at":"2026-07-05T08:41:13.579761+00:00"},{"alias_kind":"pith_short_12","alias_value":"LSTDPP2JI2KU","created_at":"2026-07-05T08:41:13.579761+00:00"},{"alias_kind":"pith_short_16","alias_value":"LSTDPP2JI2KU4R5E","created_at":"2026-07-05T08:41:13.579761+00:00"},{"alias_kind":"pith_short_8","alias_value":"LSTDPP2J","created_at":"2026-07-05T08:41:13.579761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20611","citing_title":"Estimating Learners' Skill Acquisition Without Temporal Information","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04088","citing_title":"Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4","json":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4.json","graph_json":"https://pith.science/api/pith-number/LSTDPP2JI2KU4R5ESSZ3NC4PR4/graph.json","events_json":"https://pith.science/api/pith-number/LSTDPP2JI2KU4R5ESSZ3NC4PR4/events.json","paper":"https://pith.science/paper/LSTDPP2J"},"agent_actions":{"view_html":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4","download_json":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4.json","view_paper":"https://pith.science/paper/LSTDPP2J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.05458&json=true","fetch_graph":"https://pith.science/api/pith-number/LSTDPP2JI2KU4R5ESSZ3NC4PR4/graph.json","fetch_events":"https://pith.science/api/pith-number/LSTDPP2JI2KU4R5ESSZ3NC4PR4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4/action/storage_attestation","attest_author":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4/action/author_attestation","sign_citation":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4/action/citation_signature","submit_replication":"https://pith.science/pith/LSTDPP2JI2KU4R5ESSZ3NC4PR4/action/replication_record"}},"created_at":"2026-07-05T08:41:13.579761+00:00","updated_at":"2026-07-05T08:41:13.579761+00:00"}