{"id":"W933409971","doi":"10.1016/j.chb.2015.05.041","title":"Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system","year":2015,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":214,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec-Société et Culture; American Educational Research Association; National Science Foundation","keywords":"Session (web analytics); Psychology; Intelligent tutoring system; Control (management); Self-regulated learning; Mathematics education; Student achievement; Scaffold; Process (computing); Academic achievement; Computer science; Human–computer interaction; Artificial intelligence; World Wide Web","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":[0.001931684,0.0003383884,0.0002652834,0.000419848,0.0005019744,0.002410458,0.0004908787,0.000632252,0.002636036],"category_scores_gemma":[0.0197313,0.0002910291,0.0002428091,0.00020954,0.0004212449,0.001275835,0.001006512,0.0008955704,0.0003603613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625275,"about_ca_system_score_gemma":0.0005611067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009092729,"about_ca_topic_score_gemma":0.001733365,"domain_scores_codex":[0.9991192,0.0005068531,0.00004418605,0.0001220091,0.0001184432,0.00008929555],"domain_scores_gemma":[0.980817,0.01461406,0.001856351,0.0004882462,0.0007797811,0.001444522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0083796,0.007364967,0.6371607,0.00040514,0.00079165,0.0008941368,0.008867298,0.04016721,0.1599372,0.01837307,0.002960454,0.1146986],"study_design_scores_gemma":[0.0003361513,0.002600143,0.7132505,0.00004751249,0.0007279855,0.0002374075,0.002178759,0.2380655,0.02739939,0.01302086,0.002013105,0.0001226849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948694,0.00002024649,0.002410064,0.000102016,0.000005811291,0.00001238479,0.00002407799,0.00004758807,0.002508486],"genre_scores_gemma":[0.9987335,0.000005658246,0.0008809013,0.00001080844,0.000002615233,0.00001005499,0.00001936967,0.00002073213,0.0003162987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002636036,"threshold_uncertainty_score":0.01021582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1748408877355465,"score_gpt":0.4193540647083502,"score_spread":0.2445131769728037,"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."}}