{"id":"W39470573","doi":"10.1107/s2052252524010170","title":"Learning Analytics: Readiness and Rewards","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Learning and Technology","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; National Research, Development and Innovation Office","keywords":"Turnkey; Learning analytics; Analytics; Preparedness; Computer science; Field (mathematics); Knowledge management; Data science; Business intelligence; Educational technology; Electronic learning; Mathematics education; Psychology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001922171,0.0003955875,0.0002510429,0.0008932176,0.0006296047,0.004640507,0.0006733644,0.0009466955,0.01114048],"category_scores_gemma":[0.01280891,0.0001897684,0.0002038499,0.0009933491,0.002127217,0.005957919,0.00337348,0.001941677,0.003649619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009910403,"about_ca_system_score_gemma":0.001604353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006903809,"about_ca_topic_score_gemma":0.0008250331,"domain_scores_codex":[0.9985997,0.0004704864,0.00006062117,0.0001897049,0.000435547,0.0002438834],"domain_scores_gemma":[0.9943753,0.0019036,0.0005916508,0.0006222284,0.0009658328,0.001541317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002679611,0.0002192563,0.0148948,0.0002335327,0.00003890229,0.0002539367,0.0008998847,0.006934879,0.002516972,0.6647195,0.02231467,0.2867058],"study_design_scores_gemma":[0.00004208716,0.0002041529,0.009639679,0.0001698795,0.0000213998,0.0002798915,0.001306925,0.01927138,0.003106469,0.8612402,0.1046611,0.00005694794],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.2808596,0.00523397,0.1520559,0.04707296,0.0006400624,0.0003236232,0.0009391329,0.002832515,0.5100423],"genre_scores_gemma":[0.9565153,0.001432241,0.01441256,0.0007028182,0.0001983704,0.0001196803,0.0003930689,0.0001796717,0.02604623],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01114048,"threshold_uncertainty_score":0.03726858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005173668478233113,"score_gpt":0.21735457515791,"score_spread":0.2121809066796768,"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."}}