{"id":"W4399698763","doi":"10.1080/09500693.2024.2359099","title":"Using machine learning to predict student science achievement based on science curriculum type in TIMSS 2019","year":2024,"lang":"en","type":"article","venue":"International Journal of Science Education","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Mathematics education; Curriculum; Science education; Academic achievement; Science learning; Student achievement; Computer science; Psychology; Pedagogy","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004733117,0.0001111486,0.000106637,0.00327136,0.0002997146,0.001372718,0.00316006,0.00001705871,0.000008649921],"category_scores_gemma":[0.0009245359,0.0000918419,0.00004034452,0.005310539,0.0005636425,0.002100952,0.0003187349,0.0003771679,0.000027755],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314448,"about_ca_system_score_gemma":0.009118404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007274389,"about_ca_topic_score_gemma":0.000004106631,"domain_scores_codex":[0.9958787,0.0000365167,0.0003931749,0.0004456481,0.002929955,0.0003160098],"domain_scores_gemma":[0.9976816,0.00005962227,0.0001893234,0.0002072839,0.001632351,0.0002298366],"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":[0.00002830155,0.001671281,0.1319788,0.00001743006,0.0000167016,0.00006049646,0.006237291,0.6321005,0.1162874,0.0396042,0.000194707,0.07180289],"study_design_scores_gemma":[0.0001213585,0.0003121549,0.0274336,0.000502813,0.000004457606,0.00004490992,0.0003483965,0.9674585,0.002282128,0.0003888544,0.000977751,0.0001250666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604571,0.0001035253,0.01809264,0.0120832,0.008681703,0.00009917718,9.381354e-7,0.00003084496,0.00045086],"genre_scores_gemma":[0.9738895,0.00001140014,0.02537408,0.0003185306,0.0002925163,8.24095e-7,5.802345e-7,0.000004883525,0.0001076502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.335358,"threshold_uncertainty_score":0.9996639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708207546445403,"score_gpt":0.3888061228147077,"score_spread":0.3717240473502537,"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."}}