{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AC2J2EMPDSP2ZUEW7SRVLJTWEY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"de47497925f2fdf77f8913cdae2de9995345986121241324905557ea8d7fca65","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T22:04:06Z","title_canon_sha256":"6db6e6e76a2727fd175631e610a8f927584b541c65c710788df838403226a4ed"},"schema_version":"1.0","source":{"id":"2605.12788","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12788","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12788v1","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12788","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"pith_short_12","alias_value":"AC2J2EMPDSP2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"AC2J2EMPDSP2ZUEW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"AC2J2EMP","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:e3945118cb16a097fbada38243f16c156127bfb0a3c4e240d27002eb7970987d","target":"graph","created_at":"2026-05-18T03:09:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"feature-based models reduce mean absolute error (MAE) by 22-33% relative to heuristic baselines, including fixed-percentile rules adapted from prior work"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The 425-student log dataset and the chosen features are representative enough for the models to generalize to new students and new weeks without substantial distribution shift."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Feature-based ML models forecast weekly student effort and progress in ITS with 22-33% lower MAE than percentile heuristics on data from 425 middle-school students."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Feature-based models forecast weekly student effort and progress in tutoring systems with 22-33 percent lower error than heuristic rules."}],"snapshot_sha256":"71c4411a1de2641aae68c6116713e6f38bf586cd342fc6dd2fa456634ad6c426"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Sustained effort is essential for realizing the benefits of intelligent tutoring systems (ITS), yet many learners disengage or underuse available practice time. We introduce engagement forecasting as a supervised prediction task based on ITS logs, targeting two outcomes central to effort and learning progress: minutes practiced per week and new skills mastered per week. Using interaction log data from 425 middle-school students over a school year, we benchmark fifteen predictors including regressions, decision trees, and neural networks. We show that these feature-based models reduce mean abso","authors_text":"Boyuan Guo, Conrad Borchers, Danielle R. Thomas, Eric S. Qiu, Vincent Aleven","cross_cats":["cs.CY"],"headline":"Feature-based models forecast weekly student effort and progress in tutoring systems with 22-33 percent lower error than heuristic rules.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T22:04:06Z","title":"From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning"},"references":{"count":57,"internal_anchors":0,"resolved_work":57,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"M. A. Adams, J. C. Hurley, M. Todd, N. Bhuiyan, C. L. Jarrett, W. J. Tucker, K. E. Hollingshead, and S. S. Angadi. Adaptive goal setting and financial incentives: a 2×2 factorial randomized controlled","work_id":"ee336a2b-af38-4d36-949a-1fafb7b8d61a","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"B. Albreiki, N. Zaki, and H. Alashwal. A systematic literature review of student’ performance prediction us- ing machine learning techniques.Education Sciences, 11(9):1–27, 2021","work_id":"b903e8bd-64b2-429f-9f9d-b60ba909d806","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"V. Aleven, C. Borchers, Y. Huang, T. Nagashima, B. McLaren, P. Carvalho, O. Popescu, J. Sewall, and K. Koedinger. An integrated platform for studying learning with intelligent tutoring systems: Ctat+t","work_id":"681ea6d0-c31f-45ac-828f-ab80640a7471","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"I. Arroyo, H. Meheranian, and B. P. Woolf. Effort-based tutoring: An empirical approach to intelligent tutoring. InProceedings of the 3rd International Conference on Educational Data Mining (EDM), pag","work_id":"6d5a64bd-0314-459f-8bdf-d2a6f8e3d63f","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"R. S. Baker, A. T. Corbett, and K. R. Koedinger. Detecting student misuse of intelligent tutoring sys- tems. InProceedings of the 7th International Confer- ence on Intelligent Tutoring Systems (ITS), ","work_id":"ed3228c9-3110-46bd-95f9-4f08ebabc543","year":2004}],"snapshot_sha256":"6436e51d9feaaf741c74aece5c83d55cd72a55be265fdcf515c282522a839228"},"source":{"id":"2605.12788","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:27:28.547087Z","id":"8b894eea-8e98-4cee-84c1-97cfd3b5ab56","model_set":{"reader":"grok-4.3"},"one_line_summary":"Feature-based ML models forecast weekly student effort and progress in ITS with 22-33% lower MAE than percentile heuristics on data from 425 middle-school students.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Feature-based models forecast weekly student effort and progress in tutoring systems with 22-33 percent lower error than heuristic rules.","strongest_claim":"feature-based models reduce mean absolute error (MAE) by 22-33% relative to heuristic baselines, including fixed-percentile rules adapted from prior work","weakest_assumption":"The 425-student log dataset and the chosen features are representative enough for the models to generalize to new students and new weeks without substantial distribution shift."}},"verdict_id":"8b894eea-8e98-4cee-84c1-97cfd3b5ab56"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:814bbe1670c0fb0253891d67db504e74207bbbec0a0aea93291090cf637e47bf","target":"record","created_at":"2026-05-18T03:09:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"de47497925f2fdf77f8913cdae2de9995345986121241324905557ea8d7fca65","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T22:04:06Z","title_canon_sha256":"6db6e6e76a2727fd175631e610a8f927584b541c65c710788df838403226a4ed"},"schema_version":"1.0","source":{"id":"2605.12788","kind":"arxiv","version":1}},"canonical_sha256":"00b49d118f1c9facd096fca355a676261b2d1f896204262cb88be4ae0ab7b526","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00b49d118f1c9facd096fca355a676261b2d1f896204262cb88be4ae0ab7b526","first_computed_at":"2026-05-18T03:09:12.990494Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:12.990494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tFxbJDpPobY/1Cz8P/D/Tyssb58X5jL1Bae/Ac4q/Pnh4OAfXXgzj2a6vW4oeFrkN8am6eSws9bHbMWuoej7Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:12.991132Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12788","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:814bbe1670c0fb0253891d67db504e74207bbbec0a0aea93291090cf637e47bf","sha256:e3945118cb16a097fbada38243f16c156127bfb0a3c4e240d27002eb7970987d"],"state_sha256":"8e5241d4baa872c57f99be68344549253c129b5f3e6c28c7c915b9a8b208aea0"}