{"id":"W3038189617","doi":"","title":"Personalized Learning Project: Creating and Playing Matching Games to Encourage Synthesis and Support Consolidation","year":2020,"lang":"en","type":"article","venue":"EdMedia + Innovate Learning","topic":"Educational Games and Gamification","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Consolidation (business); Computer science; Matching (statistics); Multimedia; Human–computer interaction; Business; Medicine","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":[0.001516237,0.0007823338,0.0002545087,0.0005076513,0.0007999655,0.002042195,0.001593936,0.001247455,0.02103001],"category_scores_gemma":[0.004314032,0.000359522,0.0003887824,0.0003032612,0.000779424,0.002794313,0.003197111,0.001285298,0.00284879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003852041,"about_ca_system_score_gemma":0.001295646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008274608,"about_ca_topic_score_gemma":0.00105017,"domain_scores_codex":[0.9992161,0.0003449427,0.00003363895,0.0001504635,0.0001826228,0.00007224554],"domain_scores_gemma":[0.9984971,0.000500127,0.00006524334,0.0003265598,0.0001615528,0.0004494535],"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":[0.002605218,0.007857236,0.006549901,0.0006884492,0.0001710105,0.0003543468,0.004794017,0.009839362,0.04101649,0.08439627,0.1075583,0.7341694],"study_design_scores_gemma":[0.002879811,0.00730579,0.0158277,0.0003206548,0.0004066819,0.001639798,0.0038053,0.1467267,0.1361525,0.1098115,0.5747036,0.0004199537],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2526993,0.0003920732,0.615082,0.003012433,0.001134941,0.002170842,0.001869399,0.01558737,0.1080517],"genre_scores_gemma":[0.3989,0.0002210774,0.506828,0.0007433993,0.0001175092,0.001627931,0.001252441,0.001053019,0.08925662],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02103001,"threshold_uncertainty_score":0.07035238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544606805410062,"score_gpt":0.3236522493638109,"score_spread":0.2882061813097103,"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."}}