{"id":"W2039552226","doi":"10.1145/2207243.2207248","title":"Mapping question items to skills with non-negative matrix factorization","year":2012,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Non-negative matrix factorization; Matrix decomposition; Task (project management); Variance (accounting); Machine learning; Process (computing); Matrix (chemical analysis); Artificial intelligence; Automation; Data science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003212735,0.0002145944,0.0001769905,0.0002825498,0.000308026,0.0002982607,0.0005205843,0.00007046193,0.00001855726],"category_scores_gemma":[0.0002153938,0.000181545,0.00004566347,0.0007647304,0.00001728649,0.002462961,0.0001737878,0.0001870994,0.0008543778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322679,"about_ca_system_score_gemma":0.00003412978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007741132,"about_ca_topic_score_gemma":0.000007282661,"domain_scores_codex":[0.9984357,0.0001304405,0.0003026733,0.0003497715,0.0003480504,0.0004333366],"domain_scores_gemma":[0.9986187,0.0001825056,0.0001504977,0.0006758757,0.0002141025,0.0001582837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004640735,0.0008319368,0.1440743,0.0001391844,0.0003685424,0.00002596708,0.2370605,0.04386597,0.1791833,0.3235664,0.0520613,0.01877621],"study_design_scores_gemma":[0.0015052,0.000870163,0.08026609,0.001182965,0.00005271263,0.00007895455,0.005121108,0.008711734,0.102313,0.002340025,0.7943868,0.00317127],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04080229,0.00001429557,0.9532901,0.003892508,0.001011222,0.0004223181,0.000001766766,0.0002000857,0.0003654064],"genre_scores_gemma":[0.8864707,0.000002108466,0.1071907,0.001195016,0.001238252,0.0001489863,0.00001445886,0.00002628781,0.003713462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8460994,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240098912766123,"score_gpt":0.2745934823823906,"score_spread":0.2521924932547293,"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."}}