{"id":"W2525379498","doi":"10.18608/jla.2014.13.7","title":"Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption","year":2014,"lang":"en","type":"article","venue":"Journal of Learning Analytics","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Learning analytics; Analytics; Knowledge management; Scale (ratio); Context (archaeology); Computer science; Data science; Set (abstract data type)","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":[0.1289459,0.0009946267,0.00120946,0.003920065,0.008814991,0.02062895,0.00581871,0.004407459,0.003525614],"category_scores_gemma":[0.2336674,0.001494618,0.0009814047,0.003135573,0.01773002,0.03257762,0.02569598,0.009485278,0.001392921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008175381,"about_ca_system_score_gemma":0.01719255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004854136,"about_ca_topic_score_gemma":0.004384453,"domain_scores_codex":[0.8429403,0.1169277,0.006083332,0.009656672,0.0179283,0.006463737],"domain_scores_gemma":[0.6693286,0.2439298,0.01414813,0.03688661,0.02471278,0.01099407],"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":[0.0002733994,0.001703507,0.05546672,0.00274192,0.0002092482,0.002010575,0.3607408,0.002680812,0.006446881,0.1137256,0.007849117,0.4461514],"study_design_scores_gemma":[0.0003322006,0.002067591,0.04579931,0.005163937,0.0001641772,0.001670775,0.5632976,0.008246076,0.004743083,0.2269133,0.1412002,0.0004017948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.64323,0.004095094,0.1825236,0.08492507,0.0004258719,0.003876223,0.000139456,0.00137977,0.07940498],"genre_scores_gemma":[0.9580733,0.0007206094,0.03685267,0.002120379,0.00009028357,0.0009421682,0.00003721883,0.0001332796,0.001030159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1289459,"threshold_uncertainty_score":0.6819392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008623627421887298,"score_gpt":0.2618161231192508,"score_spread":0.2531924956973636,"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."}}