{"id":"W3184975368","doi":"10.52380/ijpes.2021.8.3.239","title":"Measuring Cognitive Engagement: An Overview of Measurement Instruments and Techniques","year":2021,"lang":"en","type":"article","venue":"International Journal of Psychology and Educational Studies","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cognition; Perspective (graphical); Strengths and weaknesses; Psychology; Computer science; Measure (data warehouse); Tracking (education); Applied psychology; Data science; Social psychology; Pedagogy; Data mining; Artificial intelligence","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":[0.0006431643,0.00006161884,0.0001365047,0.0001128028,0.0000609064,0.0000271896,0.0001767711,0.00002198721,0.000008029671],"category_scores_gemma":[0.000367607,0.00005390981,0.00003020847,0.00006845596,0.00008116721,0.0002240396,0.00009821678,0.0001203665,4.856274e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002277481,"about_ca_system_score_gemma":0.0001033296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001892431,"about_ca_topic_score_gemma":0.000003259792,"domain_scores_codex":[0.9991156,0.0001083474,0.0002657904,0.000121774,0.0003290633,0.00005935549],"domain_scores_gemma":[0.9980143,0.0000916417,0.0002257351,0.0000528448,0.001578812,0.0000367202],"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":[0.0001036312,0.001799853,0.1640759,0.0001381503,0.003925641,0.00005440066,0.008753833,0.00001040355,0.005409568,0.1118991,0.001391915,0.7024376],"study_design_scores_gemma":[0.00211695,0.0008096406,0.7311574,0.002122194,0.0001933149,0.001440503,0.003620079,0.0001378291,0.008367514,0.2397211,0.009927283,0.000386224],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861423,0.05487103,0.003826664,0.05244551,0.001827714,0.00005928335,0.000007629887,0.00001267667,0.0008072535],"genre_scores_gemma":[0.9772012,0.01107347,0.01094442,0.0005688939,0.0001841367,0.000001672165,0.000001428402,0.000002270001,0.00002252618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7020514,"threshold_uncertainty_score":0.2198378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2814179123160934,"score_gpt":0.4751176393902869,"score_spread":0.1936997270741936,"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."}}