{"id":"W4307867055","doi":"10.53967/cje-rce.5455","title":"Using Robotics to Support the Acquisition of STEM and 21st-Century Competencies: Promising (and Practical) Directions","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Education / Revue canadienne de l éducation","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"","keywords":"Robotics; Curriculum; Artificial intelligence; Interpersonal communication; Intrapersonal communication; Psychology; Variety (cybernetics); Mathematics education; Computer science; Pedagogy; Robot; Communication","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001389973,0.00007771939,0.0001210837,0.0003920954,0.0006916142,0.0001529812,0.0001988771,0.0000257696,0.000009668652],"category_scores_gemma":[0.0002761674,0.00007936493,0.00003061318,0.0004564573,0.00004615673,0.0002723005,0.00002871879,0.0002918034,2.465005e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132809,"about_ca_system_score_gemma":0.004971421,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01038009,"about_ca_topic_score_gemma":0.02132071,"domain_scores_codex":[0.9989547,0.000261975,0.0002953165,0.0001483752,0.00008104592,0.0002586606],"domain_scores_gemma":[0.9985008,0.0001379904,0.0003558057,0.0002001984,0.0002732476,0.0005320021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001413761,0.000177605,0.01430933,0.0001821319,0.00009466099,0.00002561741,0.4697453,0.01556457,0.00136443,0.1015796,0.002014562,0.394928],"study_design_scores_gemma":[0.0004946367,0.001183855,0.05105546,0.0005032548,0.000266652,0.01451112,0.3138491,0.009765977,0.0001221265,0.001681515,0.6058627,0.0007035914],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672446,0.0005586554,0.01583418,0.01366909,0.002400659,0.0001960575,0.000004200188,0.000008127008,0.00008442056],"genre_scores_gemma":[0.962672,0.00001352377,0.03669143,0.0003802989,0.0001209371,0.000008869254,0.000002899449,0.000008874154,0.0001011892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6038482,"threshold_uncertainty_score":0.9965376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06510542345565024,"score_gpt":0.2999196612742654,"score_spread":0.2348142378186152,"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."}}