{"id":"W7034659990","doi":"","title":"Using EdTech Data Analytics to Promote Personalized Learning and Close Educational Gaps in Ontario","year":2025,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Learning analytics; Analytics; Data analysis; Curriculum; Survey data collection; Educational data mining; Descriptive statistics; Resource (disambiguation); Personalized learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002735249,0.0001155839,0.000191124,0.0008831851,0.00388399,0.003391674,0.001220263,0.0005048058,0.004198064],"category_scores_gemma":[0.008344699,0.0002478855,0.0002381942,0.002028194,0.001200178,0.001973262,0.005983231,0.0007119882,0.0004390797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01779445,"about_ca_system_score_gemma":0.04956959,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2837557,"about_ca_topic_score_gemma":0.5353979,"domain_scores_codex":[0.9977052,0.0004176388,0.0001191231,0.0002632568,0.0009257791,0.0005690189],"domain_scores_gemma":[0.9930298,0.001421993,0.0008691158,0.0003791118,0.001448254,0.002851725],"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":[0.0003164445,0.001330179,0.3764546,0.0005569242,0.00003795961,0.001025322,0.06714097,0.002014047,0.002966182,0.01123583,0.03175902,0.5051626],"study_design_scores_gemma":[0.00007100454,0.0006716297,0.4941623,0.0006204816,0.00005409483,0.0002522092,0.1321055,0.007422634,0.005454531,0.005737572,0.35334,0.00010812],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625436,0.0002612442,0.001691318,0.007845704,0.00003497123,0.00009624497,0.0004864894,0.0001359072,0.02690439],"genre_scores_gemma":[0.9846516,0.0003905041,0.003388471,0.0004774714,0.00001013434,0.0000659989,0.0002382049,0.00002626417,0.01075139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7162443,"threshold_uncertainty_score":0.5642081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04920287556797417,"score_gpt":0.3095337763407689,"score_spread":0.2603309007727947,"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."}}