{"id":"W7163806547","doi":"10.71305/jtl.v2i1.111","title":"Learning Analytics and Impact on Personalized Student Learning","year":2024,"lang":"","type":"article","venue":"Journal of Teaching and Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Learning analytics; Transparency (behavior); Personalized learning; Analytics; Focus group; Educational technology; Experiential learning; Qualitative research; Active learning (machine learning)","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":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01138445,0.0007258263,0.00117072,0.001257811,0.002266515,0.003751989,0.0005786147,0.0002894001,0.00005068124],"category_scores_gemma":[0.003127135,0.0005703666,0.0006683666,0.0006055642,0.0002298031,0.0009326132,0.0004178327,0.01871769,0.0000243925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002682601,"about_ca_system_score_gemma":0.0003816124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005736398,"about_ca_topic_score_gemma":7.589152e-7,"domain_scores_codex":[0.9918746,0.00387128,0.001260375,0.0008161506,0.001327058,0.0008505163],"domain_scores_gemma":[0.9951747,0.00262794,0.001069417,0.00022368,0.0002479734,0.0006562804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001486731,0.0002128654,0.05420167,0.0003857924,0.001322656,0.001060075,0.05008685,0.3534871,0.0004619182,0.00266207,0.0002209481,0.5357494],"study_design_scores_gemma":[0.001153441,0.007078775,0.008669499,0.004171115,0.0006207773,0.001719901,0.01125124,0.9202554,0.000006015792,0.0002025612,0.04414592,0.0007253994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535664,0.01732085,0.02232933,0.004486816,0.0008586765,0.00009059515,9.907442e-7,0.000183644,0.001162704],"genre_scores_gemma":[0.9804314,0.003219241,0.003448394,0.0001019167,0.001416939,5.483279e-7,0.000002361795,0.000088172,0.01129104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5667682,"threshold_uncertainty_score":0.9996748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563442431594792,"score_gpt":0.3426916693946475,"score_spread":0.3270572450786996,"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."}}