{"id":"W3038898808","doi":"10.1007/978-3-030-52237-7_23","title":"A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Personalization; Adaptation (eye); Computer science; Multimedia; Human–computer interaction; World Wide Web; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0008904445,0.0006898803,0.0007604048,0.0003930487,0.0004761075,0.001093782,0.009097119,0.000242924,0.000003706684],"category_scores_gemma":[0.0001728568,0.0006420688,0.0001402759,0.0006975415,0.0002921432,0.001045474,0.0110727,0.001522162,0.00009995288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003714142,"about_ca_system_score_gemma":0.0009022105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002676495,"about_ca_topic_score_gemma":0.00020167,"domain_scores_codex":[0.993627,0.00004301713,0.0007122864,0.002954179,0.001656181,0.00100732],"domain_scores_gemma":[0.9961034,0.0002366069,0.0003347057,0.002594145,0.0002593163,0.000471835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001031746,0.00003603658,0.00008215422,0.0000435095,0.00002748364,0.0002747556,0.004946516,0.9084243,0.0001315947,0.002698823,0.00003941692,0.08328506],"study_design_scores_gemma":[0.0002460807,0.0001986259,0.00009419007,0.0003437502,0.00001410801,0.00004443837,0.000001886187,0.9836833,0.0001418378,0.01382345,0.0006593182,0.000748972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001021173,0.00004643563,0.9910012,0.006122918,0.0005727042,0.000464747,0.0000795503,0.000311396,0.0003798288],"genre_scores_gemma":[0.3504942,0.00001379096,0.6453599,0.00278586,0.0006560285,0.000008622932,0.00002459539,0.00006276416,0.0005943118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.349473,"threshold_uncertainty_score":0.9999432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213415900172179,"score_gpt":0.3213100879639191,"score_spread":0.2791759289621973,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}