{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:JW447WXQ2KUG3FC3IPERVOBBXT","short_pith_number":"pith:JW447WXQ","schema_version":"1.0","canonical_sha256":"4db9cfdaf0d2a86d945b43c91ab821bccd98ab940d43ba38216dc279ad20f121","source":{"kind":"arxiv","id":"2105.00385","version":2},"attestation_state":"computed","paper":{"title":"pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.MS","authors_text":"Anirudhan Badrinath, Frederic Wang, Zachary Pardos","submitted_at":"2021-05-02T03:08:53Z","abstract_excerpt":"Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime w"},"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":"2105.00385","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2021-05-02T03:08:53Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"a87b12df73f048c33436b9735c46c3d4741470e1dabf52b2a3cd54d99308d2b0","abstract_canon_sha256":"b1ec90ebeae2d54ed855c6816ff7143d5dc2c31a53c2ae11b39c6a6128ec3cee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:44:18.627075Z","signature_b64":"zZsBupM6d4VH1EWU3Do0ct1Brf4mhU9gyvDgi08UWPRgxyWfIqrvVMEaveQH2JFR7AdUhgywYZoNi/nUb93zCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4db9cfdaf0d2a86d945b43c91ab821bccd98ab940d43ba38216dc279ad20f121","last_reissued_at":"2026-07-05T02:44:18.626652Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:44:18.626652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.MS","authors_text":"Anirudhan Badrinath, Frederic Wang, Zachary Pardos","submitted_at":"2021-05-02T03:08:53Z","abstract_excerpt":"Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.00385","kind":"arxiv","version":2},"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/2105.00385/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":"2105.00385","created_at":"2026-07-05T02:44:18.626702+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.00385v2","created_at":"2026-07-05T02:44:18.626702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.00385","created_at":"2026-07-05T02:44:18.626702+00:00"},{"alias_kind":"pith_short_12","alias_value":"JW447WXQ2KUG","created_at":"2026-07-05T02:44:18.626702+00:00"},{"alias_kind":"pith_short_16","alias_value":"JW447WXQ2KUG3FC3","created_at":"2026-07-05T02:44:18.626702+00:00"},{"alias_kind":"pith_short_8","alias_value":"JW447WXQ","created_at":"2026-07-05T02:44:18.626702+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.03811","citing_title":"UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT","json":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT.json","graph_json":"https://pith.science/api/pith-number/JW447WXQ2KUG3FC3IPERVOBBXT/graph.json","events_json":"https://pith.science/api/pith-number/JW447WXQ2KUG3FC3IPERVOBBXT/events.json","paper":"https://pith.science/paper/JW447WXQ"},"agent_actions":{"view_html":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT","download_json":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT.json","view_paper":"https://pith.science/paper/JW447WXQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.00385&json=true","fetch_graph":"https://pith.science/api/pith-number/JW447WXQ2KUG3FC3IPERVOBBXT/graph.json","fetch_events":"https://pith.science/api/pith-number/JW447WXQ2KUG3FC3IPERVOBBXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT/action/storage_attestation","attest_author":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT/action/author_attestation","sign_citation":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT/action/citation_signature","submit_replication":"https://pith.science/pith/JW447WXQ2KUG3FC3IPERVOBBXT/action/replication_record"}},"created_at":"2026-07-05T02:44:18.626702+00:00","updated_at":"2026-07-05T02:44:18.626702+00:00"}