{"id":"W2120399696","doi":"10.1007/3-540-47987-2_71","title":"Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Granularity; Computer science; Relevance (law); Knowledge representation and reasoning; Representation (politics); Domain (mathematical analysis); Bayesian network; Artificial intelligence; Domain knowledge; Intelligent tutoring system; Natural language processing; Artificial neural network; Machine learning; Mathematics; Programming language","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":[0.002618452,0.0002601405,0.0003503276,0.0004935723,0.0001506271,0.0002812819,0.001538133,0.0001477468,0.000003546669],"category_scores_gemma":[0.0000880148,0.0001989715,0.00006975696,0.0004364952,0.0002279862,0.0003965938,0.0008095426,0.0005861959,0.000003145963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005857556,"about_ca_system_score_gemma":0.000173857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006390583,"about_ca_topic_score_gemma":0.00005561986,"domain_scores_codex":[0.9962268,0.0002411043,0.0006061065,0.0009551434,0.001667119,0.0003036514],"domain_scores_gemma":[0.9981019,0.0002464044,0.00033679,0.0009070067,0.0003371183,0.0000707505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003485076,0.00004293083,0.005077684,0.00008602605,0.00001086921,0.00001955168,0.008971909,0.2992744,0.0002313236,0.1832511,4.968461e-7,0.5030302],"study_design_scores_gemma":[0.0001864087,0.0001155698,0.009333542,0.001459263,0.00001128527,0.00004181455,0.000006362971,0.979691,0.001347851,0.007395741,0.0001267608,0.0002844284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02840378,0.0003908369,0.9671589,0.00009729918,0.002080785,0.0007034009,6.708424e-7,0.00004365767,0.001120666],"genre_scores_gemma":[0.9884826,0.00001182521,0.01101485,0.00003142847,0.0002271792,0.00001219091,5.166054e-7,0.00001367144,0.0002057458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9600788,"threshold_uncertainty_score":0.8113821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05879284054232082,"score_gpt":0.3168170523255987,"score_spread":0.2580242117832779,"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."}}