{"id":"W4323526536","doi":"10.1145/3545947.3576235","title":"Adapting Between Parsons Problems and Coding Tasks","year":2022,"lang":"en","type":"article","venue":"","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Coding (social sciences); Computer science; Cognitive science; Artificial intelligence; Human–computer interaction; Psychology; Sociology","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":[0.003124086,0.001802683,0.0007058074,0.001227842,0.0008452486,0.003292016,0.003365157,0.002405092,0.01104563],"category_scores_gemma":[0.04382164,0.0008471177,0.0007262532,0.0009625895,0.001392342,0.004703126,0.004437583,0.003386058,0.003419284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515941,"about_ca_system_score_gemma":0.001715914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001207292,"about_ca_topic_score_gemma":0.001952071,"domain_scores_codex":[0.9920667,0.002479755,0.000931719,0.002066959,0.001859148,0.0005957641],"domain_scores_gemma":[0.9612424,0.02449931,0.002962101,0.00553557,0.003666784,0.002093874],"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":[0.002694321,0.006080542,0.01847365,0.001428869,0.00008497528,0.001442388,0.0173483,0.02451491,0.1034356,0.03007822,0.01858137,0.7758369],"study_design_scores_gemma":[0.001648158,0.00624328,0.05207472,0.00100524,0.0002051233,0.004037247,0.01061092,0.2489616,0.3094348,0.1323851,0.2321622,0.001231693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4817902,0.0001913938,0.4435841,0.001043947,0.0007028028,0.003673157,0.0007138499,0.01216685,0.05613361],"genre_scores_gemma":[0.5695526,0.0001516183,0.3927144,0.0005862824,0.00009550752,0.002739807,0.001526985,0.001565894,0.03106682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01104563,"threshold_uncertainty_score":0.0369513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04817717598437409,"score_gpt":0.2554257186185758,"score_spread":0.2072485426342017,"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."}}