{"id":"W4412496625","doi":"10.1007/978-3-031-98462-4_53","title":"Enhanced Interpretable Knowledge Tracing for Students’ Performance Prediction with Human-understandable Feature Space","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Tracing; Feature (linguistics); Space (punctuation); Artificial intelligence; Feature vector; Data mining; Machine learning; Programming language; Operating system","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":[0.0004917727,0.0006123859,0.0004790478,0.001012486,0.0001731276,0.0009808915,0.0007517991,0.000733113,0.003348386],"category_scores_gemma":[0.004250417,0.000192467,0.0004172364,0.0007738221,0.0002180902,0.001729582,0.0009988244,0.0007796201,0.001039128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002807025,"about_ca_system_score_gemma":0.0004405619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003381399,"about_ca_topic_score_gemma":0.003091783,"domain_scores_codex":[0.9996324,0.00007358198,0.00002280394,0.0001293801,0.000105386,0.0000365126],"domain_scores_gemma":[0.9979132,0.00129413,0.0001570351,0.0003354215,0.0002337454,0.00006646715],"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":[0.0004740201,0.0002737462,0.004368857,0.00008235137,0.00005027456,0.0001690491,0.0002094965,0.04636126,0.02559981,0.001955473,0.002516171,0.9179394],"study_design_scores_gemma":[0.00001034168,0.00008619366,0.002627862,0.00001539586,0.00001740642,0.00006648376,0.00002741101,0.9818978,0.01182546,0.002516055,0.000894025,0.00001546636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1241257,0.0003742353,0.8646111,0.0001215676,0.000060032,0.00004480087,0.000728158,0.007821836,0.002112511],"genre_scores_gemma":[0.8157865,0.0001506681,0.1807177,0.00002990685,0.00002518724,0.00005103173,0.0007985685,0.0001893814,0.002251053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003381399,"threshold_uncertainty_score":0.01120144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716458643122874,"score_gpt":0.2754836742118255,"score_spread":0.2583190877805967,"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."}}