{"id":"W2994680396","doi":"10.18608/jla.2019.63.9","title":"Knowledge Building Analytics to Explore Crossing Disciplinary and Grade-Level Boundaries","year":2019,"lang":"en","type":"article","venue":"Journal of Learning Analytics","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Curriculum; Agency (philosophy); Discipline; Knowledge building; Coherence (philosophical gambling strategy); Analytics; Work (physics); Mathematics education; Knowledge management; Sociology; Pedagogy; Psychology; Computer science; Data science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002522492,0.0003943806,0.0002864753,0.003448515,0.0008264199,0.004491076,0.0007939337,0.0006358312,0.006888288],"category_scores_gemma":[0.01372244,0.0001698534,0.0004833927,0.003120357,0.001049613,0.004377049,0.003279318,0.001261602,0.0009170334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00088108,"about_ca_system_score_gemma":0.001610344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008802438,"about_ca_topic_score_gemma":0.002146208,"domain_scores_codex":[0.9982578,0.0007591494,0.0001099429,0.0002504063,0.0005042169,0.0001185176],"domain_scores_gemma":[0.9859103,0.01011531,0.00133848,0.001441605,0.0006681294,0.0005261881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002308837,0.001400162,0.1411967,0.002113094,0.0001622231,0.0007462655,0.114229,0.005967204,0.01021287,0.1747574,0.01121917,0.5377649],"study_design_scores_gemma":[0.00008756046,0.0009822495,0.1373038,0.002941191,0.0001703836,0.001219052,0.09876028,0.04606862,0.01921924,0.4068125,0.2863017,0.0001334553],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5132127,0.00171365,0.3398896,0.004424085,0.000115106,0.0008268681,0.00295858,0.003044419,0.133815],"genre_scores_gemma":[0.8551889,0.0004832978,0.1373366,0.0002343864,0.00003027098,0.0003912933,0.001503307,0.0001315075,0.004700406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006888288,"threshold_uncertainty_score":0.02304363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1819360702718453,"score_gpt":0.4544701751076211,"score_spread":0.2725341048357757,"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."}}