{"id":"W2936846186","doi":"10.1007/978-3-030-11434-3_36","title":"An Active Learning Strategy for Programming Courses","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Innovative Teaching Methods","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hamilton Health Sciences","funders":"","keywords":"Debugging; Active learning (machine learning); Recall; Class (philosophy); Mathematics education; Computer science; Coding (social sciences); Reinforcement; Psychology; Artificial intelligence; Programming language; Cognitive psychology; Mathematics; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002740345,0.0003082487,0.0005729599,0.0002066393,0.0005680358,0.0002145855,0.0002760142,0.0002773986,0.000009999753],"category_scores_gemma":[0.0001955432,0.0003130173,0.00007498272,0.00007538819,0.000227855,0.0003472291,0.0000520592,0.0006828507,0.000004798178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482959,"about_ca_system_score_gemma":0.0001793198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003609797,"about_ca_topic_score_gemma":0.0003070852,"domain_scores_codex":[0.9977549,0.0003341924,0.0005598193,0.0005696719,0.0003187894,0.0004625753],"domain_scores_gemma":[0.9978213,0.001038055,0.0006514982,0.0001604297,0.0002571175,0.00007163512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001473481,0.00001106916,0.0004454789,0.0001702304,0.00002234001,0.000002137091,0.00440699,0.006456152,0.000003359349,0.4964156,0.000003070234,0.4920488],"study_design_scores_gemma":[0.0001837886,0.0004122979,0.00002288081,0.002060137,0.00002734622,0.000004206463,0.02701239,0.005730012,0.00002244839,0.007957632,0.9558961,0.0006707744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002946358,0.01566391,0.4341847,0.00003422767,0.004179292,0.003874734,0.00001755582,0.0003187143,0.5387805],"genre_scores_gemma":[0.8563006,0.001979433,0.0195961,0.00004157015,0.002622022,0.00006266873,0.00006808581,0.0001861173,0.1191434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.955893,"threshold_uncertainty_score":0.9999322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06744129641568486,"score_gpt":0.4193019224435182,"score_spread":0.3518606260278334,"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."}}