{"id":"W2894828986","doi":"","title":"Three Stories on Learning Analytics Show How Far Institutions Can Go With Data","year":2018,"lang":"en","type":"article","venue":"University of Groningen research database (University of Groningen / Centre for Information Technology)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Learning analytics; Analytics; Data science; Data analysis; Blackboard (design pattern); Computer science; Software analytics; Knowledge management; Data mining; Software","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01840174,0.001196366,0.0004652388,0.002067308,0.01444971,0.02057603,0.001922365,0.007884293,0.007588366],"category_scores_gemma":[0.05808035,0.0006902622,0.001202139,0.003132983,0.0192601,0.03100548,0.0144722,0.01833392,0.002491907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007276291,"about_ca_system_score_gemma":0.004028624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01396687,"about_ca_topic_score_gemma":0.02898071,"domain_scores_codex":[0.9703439,0.01784694,0.0006224056,0.0007798414,0.007679789,0.002726944],"domain_scores_gemma":[0.9433776,0.03757474,0.002184321,0.002823235,0.007022252,0.007017767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001527742,0.00005801642,0.003290312,0.0002467543,0.00003897702,0.001313259,0.1894126,0.000270826,0.0008745515,0.095838,0.6721346,0.03636933],"study_design_scores_gemma":[0.000006993108,0.00001828081,0.0006437882,0.000173516,0.00000612622,0.0003905648,0.07092245,0.0001789746,0.000481212,0.01241672,0.9147132,0.00004824485],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03509708,0.005262013,0.01265032,0.7936819,0.005323065,0.0001257525,0.0009241348,0.0006855624,0.1462501],"genre_scores_gemma":[0.5803144,0.008355577,0.02084739,0.2470096,0.003888028,0.0004381213,0.001742911,0.001902683,0.1355013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02057603,"threshold_uncertainty_score":0.09731889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07241787658136735,"score_gpt":0.2888101554915219,"score_spread":0.2163922789101546,"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."}}