{"id":"W4394891968","doi":"10.3390/educsci14040423","title":"Learning Multiplication by Translating across Microworlds","year":2024,"lang":"en","type":"article","venue":"Education Sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Multiplication (music); Mathematics education; Computer science; Arithmetic; Psychology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003292537,0.0007828298,0.000329592,0.0005838185,0.001389187,0.005576217,0.0008955678,0.001217629,0.006202959],"category_scores_gemma":[0.01231376,0.0002806575,0.0004432778,0.0005521712,0.003973579,0.006074078,0.004637505,0.001840047,0.001244649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005859961,"about_ca_system_score_gemma":0.0006123086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002746088,"about_ca_topic_score_gemma":0.0005351081,"domain_scores_codex":[0.995505,0.003175497,0.0001178155,0.0004506835,0.0004737669,0.0002772016],"domain_scores_gemma":[0.9952787,0.003086027,0.0004491661,0.0005054041,0.0002365524,0.0004441063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003487291,0.001221405,0.02271896,0.0006057424,0.00004709815,0.001805495,0.6300885,0.003294019,0.0264387,0.08992862,0.005686308,0.2178165],"study_design_scores_gemma":[0.0001442325,0.002247482,0.01904364,0.0004692368,0.00007416343,0.003001573,0.5747546,0.01357029,0.01830265,0.1025382,0.2656322,0.000221707],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8994244,0.0003090845,0.05170137,0.002070256,0.0001022557,0.0001599502,0.0000251635,0.0001753309,0.04603219],"genre_scores_gemma":[0.9730102,0.0002934061,0.01851375,0.0002643155,0.00002374642,0.0001223082,0.00005306413,0.00005941722,0.007659735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006202959,"threshold_uncertainty_score":0.020751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076342889523525,"score_gpt":0.3483114264254637,"score_spread":0.3275479975302285,"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."}}