{"id":"W2904577629","doi":"10.4018/978-1-5225-1005-5.ch006","title":"Tracking Children's Interactions with Traditional Text and Computer-Based Early Literacy Media","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in educational technologies and instructional design book series","topic":"Child Development and Digital Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Tracking (education); Computer science; Software; Literacy; Early literacy; Eye tracking; Focus (optics); Psychological intervention; Multimedia; Key (lock); Psychology; Human–computer interaction; Pedagogy; 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":[0.0004819266,0.0004630657,0.0002894452,0.001047762,0.00033872,0.001461067,0.0005074469,0.000616417,0.007345472],"category_scores_gemma":[0.002000127,0.000273108,0.000284136,0.001180083,0.0004186043,0.001745305,0.0008962598,0.0007050278,0.002574212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005166964,"about_ca_system_score_gemma":0.0005215766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003211119,"about_ca_topic_score_gemma":0.007346706,"domain_scores_codex":[0.9997631,0.00002596403,0.00001731508,0.00005784065,0.0001115617,0.00002427823],"domain_scores_gemma":[0.9992938,0.000495894,0.00008876686,0.00003272615,0.00006242991,0.00002634957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007266551,0.0001549487,0.02852784,0.001194447,0.00001986891,0.0006025221,0.02856439,0.0006792208,0.04568169,0.01055176,0.01224028,0.8717104],"study_design_scores_gemma":[0.00003056173,0.0006127381,0.4005143,0.003268236,0.0001043309,0.006148744,0.01961746,0.002146665,0.05820147,0.01155426,0.4976049,0.0001962594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6462091,0.0264652,0.0398986,0.001689746,0.0002988298,0.0002864757,0.004614906,0.001821208,0.278716],"genre_scores_gemma":[0.6335209,0.06689002,0.1039352,0.0007695497,0.00009022738,0.0007671544,0.005282986,0.0007068928,0.1880371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007345472,"threshold_uncertainty_score":0.02457303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009615857908869,"score_gpt":0.2589829380080732,"score_spread":0.2388867794289845,"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."}}