{"id":"W3038090822","doi":"10.1016/j.chb.2020.106457","title":"Construct and consequential validity for learning analytics based on trace data","year":2020,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"TRACE (psycholinguistics); Learning analytics; Analytics; Data science; Construct (python library); Computer science; Data analysis; Set (abstract data type); Reliability (semiconductor); Construct validity; Validity; Psychology; Data mining; Psychometrics","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":[0.0003253853,0.0001700236,0.0002622846,0.0001151168,0.000187117,0.0002364895,0.0009993953,0.00006971748,0.000005032541],"category_scores_gemma":[0.0001108912,0.0001827654,0.00005334019,0.0002150737,0.0001252903,0.0001695576,0.0003637166,0.0003775897,0.000002541069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002616407,"about_ca_system_score_gemma":0.0000733171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007420248,"about_ca_topic_score_gemma":0.000003706712,"domain_scores_codex":[0.9985009,0.0001225705,0.0002825427,0.0006487434,0.0001951166,0.0002501228],"domain_scores_gemma":[0.9988856,0.0002813615,0.0001163385,0.0005280986,0.0000448795,0.0001436719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001755156,0.001739217,0.5330704,0.0006473769,0.00011103,0.001171048,0.003272449,0.1289207,0.00437172,0.05493216,0.00967803,0.2619104],"study_design_scores_gemma":[0.0009986267,0.0003879533,0.00503694,0.00004143466,0.0000393044,0.0000063973,0.00002091035,0.9918647,0.00008190431,0.0001753833,0.001121952,0.0002245041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3322504,0.00001773957,0.6644266,0.002524616,0.0002478746,0.0002732881,0.00002521385,0.0001925571,0.00004170316],"genre_scores_gemma":[0.9122776,0.000001709488,0.08706654,0.0004354703,0.0001246293,0.000005205229,0.00006058134,0.00001337592,0.00001483288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.862944,"threshold_uncertainty_score":0.7452956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1571785384843767,"score_gpt":0.3558847405011091,"score_spread":0.1987062020167324,"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."}}