{"id":"W2045683665","doi":"10.1007/s10956-015-9550-z","title":"Empowering Prospective Teachers to Become Active Sense-Makers: Multimodal Modeling of the Seasons","year":2015,"lang":"en","type":"article","venue":"Journal of Science Education and Technology","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Affordance; Educational technology; Informal learning; Learning sciences; Embodied cognition; Science education; Mathematics education; Instructional design; Active learning (machine learning); Computer science; Pedagogy; Psychology; Human–computer interaction; Artificial intelligence","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":[0.0006757076,0.0002257923,0.0001578993,0.0002045149,0.0005441515,0.003070323,0.0005296306,0.0005669127,0.006554549],"category_scores_gemma":[0.003550589,0.0001987917,0.0003023395,0.0002733331,0.000726946,0.002114897,0.001085248,0.0004871181,0.0003841881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004246398,"about_ca_system_score_gemma":0.0006167582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909315,"about_ca_topic_score_gemma":0.006020967,"domain_scores_codex":[0.9997262,0.0001647892,0.000007071896,0.000044482,0.0000223773,0.00003502123],"domain_scores_gemma":[0.9988456,0.0008286712,0.0000823019,0.00008400227,0.00006055894,0.0000988286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00210033,0.001661996,0.1424767,0.0009860577,0.0002314777,0.001106244,0.2919212,0.1309697,0.02948673,0.1961314,0.01021583,0.1927124],"study_design_scores_gemma":[0.0002041658,0.0008372047,0.1024038,0.0003323028,0.0002268993,0.0003458521,0.1427083,0.60584,0.004302807,0.1003404,0.04224757,0.0002107995],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8990365,0.0001341162,0.05996108,0.0008213786,0.00007863551,0.0001132531,0.0002411086,0.0001575502,0.03945646],"genre_scores_gemma":[0.9939297,0.0000410532,0.004204968,0.0000211046,0.000004324929,0.00004031994,0.00002774145,0.00001778327,0.001712977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006554549,"threshold_uncertainty_score":0.02192712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582486241636294,"score_gpt":0.3199674426734987,"score_spread":0.3041425802571358,"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."}}