{"id":"W2980954366","doi":"10.1187/cbe.19-02-0041","title":"Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory","year":2019,"lang":"en","type":"article","venue":"CBE—Life Sciences Education","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Biostatistics; Scripting language; Curriculum; Computer science; Cognitive load; Software; Point (geometry); Mathematics education; Cognition; Psychology; Programming language; Medicine; Mathematics; Pedagogy; Pathology; Public health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01339487,0.0008114779,0.0008276953,0.00310672,0.001389795,0.00393135,0.00178644,0.0008011662,0.007140949],"category_scores_gemma":[0.05154794,0.0006105262,0.000737941,0.001514908,0.001189534,0.001709821,0.004499885,0.002281266,0.00249761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001966366,"about_ca_system_score_gemma":0.005249911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006395336,"about_ca_topic_score_gemma":0.001477507,"domain_scores_codex":[0.9935091,0.002140184,0.0005405435,0.001078259,0.002111105,0.0006207202],"domain_scores_gemma":[0.9540486,0.02139889,0.005141986,0.004926957,0.00681672,0.007666763],"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":[0.0006684266,0.01659703,0.1047312,0.0008393372,0.0001120613,0.0004318276,0.01148804,0.005868032,0.02626639,0.009090848,0.0133789,0.810528],"study_design_scores_gemma":[0.0008675251,0.01258613,0.5489505,0.001138447,0.0002435334,0.002389218,0.0108213,0.1166161,0.07285474,0.1235792,0.1094256,0.0005277986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8336085,0.0002800229,0.1329724,0.002487064,0.0002366288,0.003172629,0.0003149136,0.001896918,0.02503097],"genre_scores_gemma":[0.7767408,0.000352368,0.2130487,0.0009446188,0.000104459,0.001938279,0.0004735542,0.0001982242,0.006198895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01339487,"threshold_uncertainty_score":0.07083964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04982055719216508,"score_gpt":0.3954892782447707,"score_spread":0.3456687210526056,"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."}}