{"id":"W2903443219","doi":"10.33524/cjar.v19i2.383","title":"ROBOTICS AND MATH: USING ACTION RESEARCH TO STUDY GROWTH PROBLEMS","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Action Research","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Robotics; Artificial intelligence; Curriculum; Action research; Mathematics education; Proportional reasoning; Action (physics); Robot; Domain (mathematical analysis); Inclusion (mineral); Computer science; Psychology; Mathematics; Pedagogy; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01495153,0.00007068076,0.0001042374,0.001050244,0.00266148,0.001105303,0.0007981014,0.00005125696,0.000009091284],"category_scores_gemma":[0.0008564989,0.00005190109,0.00002471327,0.001335479,0.0002508082,0.0003863487,0.0001231948,0.001650095,0.0000279761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004422127,"about_ca_system_score_gemma":0.001047638,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03845038,"about_ca_topic_score_gemma":0.04385424,"domain_scores_codex":[0.9963694,0.001706568,0.0002125546,0.0001882728,0.0009466903,0.0005764671],"domain_scores_gemma":[0.9972494,0.0002957033,0.00007561045,0.0002991281,0.0015277,0.000552467],"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":[0.0001309472,0.0002945913,0.0275455,0.00007468699,0.0002251549,0.0002589624,0.1229875,0.005330855,0.004758461,0.015991,0.004529986,0.8178724],"study_design_scores_gemma":[0.00494736,0.03601255,0.3327546,0.001585003,0.0001336646,0.006585997,0.1131177,0.1445473,0.005922813,0.02573951,0.3267108,0.001942804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575287,0.00006835535,0.03131792,0.01011911,0.0004724031,0.0003499506,2.066879e-7,0.0000161528,0.0001271644],"genre_scores_gemma":[0.9896349,0.000005179214,0.009655931,0.00003044123,0.000433341,0.000002837651,5.316288e-8,0.0000107274,0.0002265285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8159296,"threshold_uncertainty_score":0.9999316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4505220646980868,"score_gpt":0.4822360617483719,"score_spread":0.0317139970502851,"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."}}