{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GHQZHTUDHZ37AK7NAL4DGJ33CV","short_pith_number":"pith:GHQZHTUD","schema_version":"1.0","canonical_sha256":"31e193ce833e77f02bed02f833277b155a4015cd1f6015c9bfdaa506c72b0205","source":{"kind":"arxiv","id":"1812.05044","version":2},"attestation_state":"computed","paper":{"title":"Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dit-Yan Yeung, Kai Yang, Mucong Ding, Ting-Chuen Pong","submitted_at":"2018-12-12T17:30:27Z","abstract_excerpt":"The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw featur"},"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":"1812.05044","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-12T17:30:27Z","cross_cats_sorted":["cs.CY","stat.ML"],"title_canon_sha256":"497ed95cbbb4fd8badf0655d38c35fe834e9ed475c441b2b420ae854ac12b602","abstract_canon_sha256":"8cd0b42e743c1b657ff53e9c8dbbad17752b041a3b277e42931cceb9955404a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:56.754355Z","signature_b64":"0TiU+zh9xOEyu2FZ8pVjuJSOPmDulllgI/hzkocxxu86X1EN9zVVt/iEbkHeHkO4uLSj149exh7MgmqHfV5FAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31e193ce833e77f02bed02f833277b155a4015cd1f6015c9bfdaa506c72b0205","last_reissued_at":"2026-05-17T23:57:56.753598Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:56.753598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dit-Yan Yeung, Kai Yang, Mucong Ding, Ting-Chuen Pong","submitted_at":"2018-12-12T17:30:27Z","abstract_excerpt":"The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw featur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05044","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":""},"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":"1812.05044","created_at":"2026-05-17T23:57:56.753724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.05044v2","created_at":"2026-05-17T23:57:56.753724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05044","created_at":"2026-05-17T23:57:56.753724+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHQZHTUDHZ37","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHQZHTUDHZ37AK7N","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHQZHTUD","created_at":"2026-05-18T12:32:25.280505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV","json":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV.json","graph_json":"https://pith.science/api/pith-number/GHQZHTUDHZ37AK7NAL4DGJ33CV/graph.json","events_json":"https://pith.science/api/pith-number/GHQZHTUDHZ37AK7NAL4DGJ33CV/events.json","paper":"https://pith.science/paper/GHQZHTUD"},"agent_actions":{"view_html":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV","download_json":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV.json","view_paper":"https://pith.science/paper/GHQZHTUD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.05044&json=true","fetch_graph":"https://pith.science/api/pith-number/GHQZHTUDHZ37AK7NAL4DGJ33CV/graph.json","fetch_events":"https://pith.science/api/pith-number/GHQZHTUDHZ37AK7NAL4DGJ33CV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV/action/storage_attestation","attest_author":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV/action/author_attestation","sign_citation":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV/action/citation_signature","submit_replication":"https://pith.science/pith/GHQZHTUDHZ37AK7NAL4DGJ33CV/action/replication_record"}},"created_at":"2026-05-17T23:57:56.753724+00:00","updated_at":"2026-05-17T23:57:56.753724+00:00"}