{"id":"W2885041381","doi":"","title":"Learning with Multiple Representations: Infographics as Cognitive Tools for Authentic Learning in Science Literacy.","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Learning and Technology","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Infographic; Mathematics education; Cognition; Educational technology; Science education; Teaching method; Science learning; Literacy; Psychology; Scientific literacy; Learning sciences; Metacognition; Computer science; Pedagogy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00354151,0.0001734308,0.0003179105,0.003290828,0.001092333,0.0002026262,0.0002464328,0.0002082446,0.00008340256],"category_scores_gemma":[0.01799366,0.0001606889,0.00004195569,0.002333725,0.001820848,0.0003312448,0.00002721382,0.002361372,0.00001196546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007836529,"about_ca_system_score_gemma":0.00076948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008876429,"about_ca_topic_score_gemma":0.0008995674,"domain_scores_codex":[0.9979216,0.0005282313,0.0004327762,0.0003501731,0.0001539662,0.0006132454],"domain_scores_gemma":[0.996686,0.001283682,0.0005092243,0.0001138316,0.001205004,0.0002022936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001359449,0.0000175929,0.806486,0.00001092204,0.0000641829,0.0001037837,0.02089854,0.0001002823,0.0005813868,0.003979838,0.00001320411,0.1676083],"study_design_scores_gemma":[0.01263075,0.024974,0.419803,0.002716286,0.0002785875,0.005231316,0.3292549,0.003534911,0.002335921,0.006687637,0.1908621,0.001690596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879456,0.0003628719,0.007905343,0.0006466599,0.0002554295,0.0001729987,0.000001014863,0.00006472086,0.002645435],"genre_scores_gemma":[0.9932575,0.000009253647,0.005176294,0.00003766683,0.0001095419,0.00001827966,0.000004024347,0.00003086134,0.001356588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.386683,"threshold_uncertainty_score":0.9999402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02745940785150799,"score_gpt":0.379569032404356,"score_spread":0.352109624552848,"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."}}