{"id":"W1498982155","doi":"10.18608/jla.2014.12.4","title":"Learning Analytics for Online Discussions: Embedded and Extracted Approaches","year":2014,"lang":"en","type":"article","venue":"Journal of Learning Analytics","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Learning analytics; Analytics; Computer science; Software analytics; Educational technology; Data science; Intervention (counseling); Asynchronous communication; Mathematics education; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00223265,0.0003059015,0.0007042114,0.000548579,0.0004144273,0.0003847229,0.0007111384,0.000180851,0.000005129727],"category_scores_gemma":[0.005411505,0.0002349392,0.0003357962,0.0006769997,0.000105901,0.0004589019,0.000181789,0.001568901,0.000003636279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005737961,"about_ca_system_score_gemma":0.0001368738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002378122,"about_ca_topic_score_gemma":0.000003313756,"domain_scores_codex":[0.9972215,0.0003917651,0.0009600769,0.0003799342,0.0005720385,0.0004747111],"domain_scores_gemma":[0.9961823,0.001355205,0.001272477,0.0003399262,0.0005132672,0.0003368107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000934015,0.000658838,0.0341644,0.0002289913,0.0006277207,0.00004756133,0.002543835,0.7120887,0.0006746841,0.01374484,0.000899508,0.2342275],"study_design_scores_gemma":[0.0009112257,0.00104815,0.002782965,0.0001717979,0.0002294849,0.0001275548,0.001176032,0.9697405,0.00003747775,0.002003721,0.02145537,0.0003157656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1475812,0.0003209898,0.8438632,0.00747107,0.0002585808,0.0001058149,0.000002357357,0.0001361357,0.0002606851],"genre_scores_gemma":[0.863739,0.000181465,0.1324946,0.00009031036,0.0006167101,9.106142e-7,0.00001237338,0.00003837577,0.002826181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7161579,"threshold_uncertainty_score":0.9580542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04217981932674447,"score_gpt":0.2891730724093879,"score_spread":0.2469932530826434,"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."}}