{"id":"W4392721310","doi":"10.22318/icls2023.101292","title":"Detection of Goal Setting and Planning in Self-regulated Learning Using Machine Learning and Think-aloud Protocols","year":2023,"lang":"en","type":"article","venue":"Proceedings.","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Think aloud protocol; Computer science; Decision tree; Machine learning; Artificial intelligence; Random forest; Artificial neural network; Support vector machine; Logistic regression; Online machine learning; Coding (social sciences); Natural language processing; Human–computer interaction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004283415,0.0002136087,0.0003349792,0.000554871,0.0003608948,0.00006807051,0.00007751907,0.0001965284,0.00001397227],"category_scores_gemma":[0.001053458,0.0002154522,0.00002800648,0.001051471,0.00007272721,0.0001585218,0.0001125975,0.00134555,0.000003797524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004897837,"about_ca_system_score_gemma":0.00001521965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001361158,"about_ca_topic_score_gemma":0.000001614606,"domain_scores_codex":[0.9982213,0.0003161216,0.0004195897,0.0004453209,0.0001796831,0.0004179912],"domain_scores_gemma":[0.99922,0.0001860043,0.0003671574,0.00005034434,0.0001259363,0.00005063176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001654417,0.00003577506,0.7185824,0.000323283,0.00006630376,0.00001269075,0.1107372,0.0003412931,0.1421318,0.0002868795,0.000007669709,0.02730929],"study_design_scores_gemma":[0.003914256,0.0009620111,0.6421208,0.001615674,0.00007858525,0.0002363328,0.04324992,0.2910994,0.01167957,0.0005859809,0.003524731,0.0009327559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995884,0.000144644,0.0004578258,0.00002354915,0.00008794868,0.001494439,5.384692e-7,0.0005220355,0.001385005],"genre_scores_gemma":[0.994027,0.000003248776,0.005372088,0.00001091741,0.0001091935,0.0001192042,0.000003931713,0.00005269056,0.0003017822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2907581,"threshold_uncertainty_score":0.8785886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04454667977794718,"score_gpt":0.3909294621899841,"score_spread":0.346382782412037,"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."}}