{"id":"W2170666364","doi":"10.1145/2567574.2567592","title":"Setting learning analytics in context","year":2014,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Learning analytics; Analytics; Computer science; Context (archaeology); Process (computing); Scale (ratio); Data science; Set (abstract data type); Knowledge management; Institution","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":[],"consensus_categories":[],"category_scores_codex":[0.0005458361,0.00006849815,0.0001093238,0.0001069035,0.00005592667,0.00009771241,0.0003224823,0.00003335367,0.00001115286],"category_scores_gemma":[0.0002908502,0.00006109087,0.00003290033,0.0003187914,0.00001288317,0.0001326614,0.0001063284,0.0002514824,0.0001004455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001510839,"about_ca_system_score_gemma":0.00002025022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003707271,"about_ca_topic_score_gemma":0.00004980127,"domain_scores_codex":[0.9992376,0.00008787314,0.0001563419,0.0001934085,0.0001245733,0.0002002294],"domain_scores_gemma":[0.9995193,0.0001586505,0.00004752,0.000195603,0.00003132944,0.00004758403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000127808,0.00005707253,0.1528741,0.00001541993,0.00001097831,0.00001331208,0.001102883,0.04685348,0.0001624971,0.3514079,0.0005348392,0.4469662],"study_design_scores_gemma":[0.0001536468,0.00003476438,0.002928618,0.00001483806,0.000001322381,0.000002445977,0.0001163824,0.9819003,0.00006582842,0.002194894,0.01249398,0.00009292505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07671323,0.00002412101,0.8848762,0.008747039,0.0001042187,0.00003170252,5.53171e-8,0.0003015324,0.02920188],"genre_scores_gemma":[0.9719295,0.000002373424,0.02430437,0.0005463614,0.00005321849,4.104969e-7,6.715115e-7,0.00000452714,0.003158596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9350469,"threshold_uncertainty_score":0.2491213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008793174781213225,"score_gpt":0.2506948190231344,"score_spread":0.2419016442419212,"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."}}