{"id":"W2332622947","doi":"10.1109/csicsse.2015.7369246","title":"Design practices for multimodal affective mathematical learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Industry Canada","keywords":"Modalities; Distraction; Computer science; Affect (linguistics); Multimodal learning; Human–computer interaction; Affective computing; Multimodal interaction; Cognition; Equivalence (formal languages); Multimodal therapy; Focus (optics); Cognitive psychology; Artificial intelligence; Psychology; Psychotherapist","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":[0.01558547,0.001256281,0.0003215453,0.001738786,0.001528358,0.005031244,0.002150356,0.001564421,0.005815147],"category_scores_gemma":[0.02362721,0.0007327379,0.0006723285,0.000700385,0.00336949,0.003539729,0.003759582,0.001484269,0.001500811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408289,"about_ca_system_score_gemma":0.001136867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007049519,"about_ca_topic_score_gemma":0.001000832,"domain_scores_codex":[0.9839749,0.0112175,0.0007396967,0.001130403,0.002493064,0.0004445125],"domain_scores_gemma":[0.9888808,0.006148947,0.0006818403,0.001844243,0.001980823,0.0004634463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002436056,0.000609455,0.004776414,0.003313091,0.0001685758,0.0009455502,0.09672792,0.01182547,0.05857807,0.3186623,0.008488894,0.4956608],"study_design_scores_gemma":[0.0003150037,0.001794602,0.005285512,0.003431042,0.0003576585,0.003487136,0.02877963,0.05590341,0.0583194,0.202073,0.6399422,0.0003114318],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02419338,0.0006944558,0.9496636,0.001331489,0.00008498321,0.0006580287,0.00003231766,0.0009942204,0.02234766],"genre_scores_gemma":[0.2189178,0.0004901141,0.7712685,0.0003106446,0.00002467107,0.001428876,0.000061649,0.0003801968,0.007117462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01558547,"threshold_uncertainty_score":0.08242482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2842069119576437,"score_gpt":0.5011968603609537,"score_spread":0.21698994840331,"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."}}