{"id":"W2482643417","doi":"10.4018/978-1-4666-9634-1.ch016","title":"Concept Science Evidence-Based MERLO Learning Analytics","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in educational technologies and instructional design book series","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Learning analytics; Informatics; Computer science; Analytics; Science learning; Meaning (existential); Mathematics education; Data science; Learning sciences; Knowledge management; Educational technology; Science education; Psychology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264331,0.0006151974,0.000417241,0.002661378,0.0005970212,0.004935694,0.0007586856,0.0006389536,0.01805293],"category_scores_gemma":[0.003774935,0.0002737671,0.0004176321,0.002232089,0.001361877,0.005368786,0.001907718,0.002239687,0.005989228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709959,"about_ca_system_score_gemma":0.002604346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008496682,"about_ca_topic_score_gemma":0.002319868,"domain_scores_codex":[0.9991655,0.0001340187,0.00004485969,0.00009222492,0.0005305292,0.00003284419],"domain_scores_gemma":[0.998121,0.00114751,0.0001007681,0.0001160854,0.0004178719,0.00009677459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001570056,0.00007333045,0.0003945044,0.0009715786,0.00001220462,0.00008866877,0.001706499,0.001114029,0.0008591937,0.3425054,0.1859962,0.4662626],"study_design_scores_gemma":[0.000003305945,0.00001559654,0.0003986087,0.001015224,0.000005269395,0.0001689312,0.000338345,0.0008109193,0.0004750105,0.08030131,0.9164561,0.00001138891],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.005800603,0.04809067,0.1215336,0.0139001,0.003755088,0.000371975,0.001145993,0.001331334,0.8040705],"genre_scores_gemma":[0.06214082,0.07826845,0.1616294,0.006624966,0.002449674,0.0005864468,0.003349592,0.0009956426,0.683955],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01805293,"threshold_uncertainty_score":0.0603931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04357824006544862,"score_gpt":0.3043174230339099,"score_spread":0.2607391829684613,"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."}}