{"id":"W2796378979","doi":"10.1111/dsji.12148","title":"Teaching Line Balancing through Active and Blended Learning*","year":2018,"lang":"en","type":"article","venue":"Decision Sciences Journal of Innovative Education","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Wilfrid Laurier University","funders":"","keywords":"Computer science; Workforce; Class (philosophy); Curriculum; Process (computing); Active learning (machine learning); Blended learning; Assembly line; Teaching method; Multimedia; Mathematics education; Knowledge management; Educational technology; Pedagogy; Engineering; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030779,0.0007658558,0.0003919314,0.0005586567,0.0004532723,0.001950387,0.001606009,0.0008311559,0.008927326],"category_scores_gemma":[0.002492863,0.0001892534,0.0004449136,0.0003995576,0.0004074966,0.001520492,0.002155052,0.0009522921,0.00259639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000386315,"about_ca_system_score_gemma":0.000638616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000150678,"about_ca_topic_score_gemma":0.0002535371,"domain_scores_codex":[0.9990667,0.0002559311,0.00005117225,0.0001880085,0.0003228306,0.0001153396],"domain_scores_gemma":[0.9983193,0.0006353381,0.0001720323,0.0001752566,0.0002260147,0.0004719881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000674037,0.01410715,0.003648732,0.0005799661,0.00003741942,0.0002967523,0.002549292,0.009221753,0.04680612,0.008525182,0.008992123,0.9045615],"study_design_scores_gemma":[0.001663219,0.02295236,0.02589956,0.001884813,0.0002917581,0.003150883,0.00699943,0.28321,0.2794728,0.0951736,0.2788828,0.0004189218],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5391288,0.0004646283,0.4054967,0.001114223,0.0004569672,0.000958334,0.0001705291,0.002310829,0.04989893],"genre_scores_gemma":[0.7241958,0.0004925263,0.2511623,0.0005195148,0.0001326482,0.0006062344,0.0002094381,0.0001237208,0.02255785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008927326,"threshold_uncertainty_score":0.02986485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04572558347519932,"score_gpt":0.3676041846173795,"score_spread":0.3218786011421802,"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."}}