{"id":"W2647154366","doi":"10.1007/978-3-319-61425-0_10","title":"Personalized Tag-Based Knowledge Diagnosis to Predict the Quality of Answers in a Community of Learners","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Ranging; Quality (philosophy); Mean squared error; Field (mathematics); Sample (material); Naive Bayes classifier; Artificial intelligence; Machine learning; Data science; Statistics; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005158454,0.0003782739,0.0007928062,0.0006579239,0.0003763009,0.0001810933,0.00513506,0.0002071799,0.000008562057],"category_scores_gemma":[0.0008288455,0.0002896857,0.0002214613,0.0003929018,0.001142683,0.000236881,0.001106966,0.00122326,0.000006636325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000258953,"about_ca_system_score_gemma":0.0006628602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477342,"about_ca_topic_score_gemma":0.00105062,"domain_scores_codex":[0.9966862,0.0004769267,0.0008006986,0.0007035678,0.0008799128,0.0004527588],"domain_scores_gemma":[0.9945257,0.002272319,0.0007518206,0.001958635,0.0003843448,0.0001072203],"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":[0.00009918666,0.0004141605,0.02318454,0.0009338749,0.00007810249,0.00003117833,0.06086655,0.2332415,0.0007733637,0.1391874,0.00004143672,0.5411488],"study_design_scores_gemma":[0.004878218,0.00466848,0.06090854,0.02984937,0.00008466908,0.00003564235,0.0001462085,0.7541503,0.02796435,0.05000539,0.06189138,0.005417423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006220468,0.0004511173,0.9888231,0.0004786379,0.0009196313,0.0005694927,0.000009814738,0.00003921544,0.002488479],"genre_scores_gemma":[0.9768146,0.00001519746,0.02208675,0.0002222652,0.0001096058,0.00002398654,0.000001629722,0.00002023752,0.0007057221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9705941,"threshold_uncertainty_score":0.9999555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06173944265229001,"score_gpt":0.3258761021413216,"score_spread":0.2641366594890315,"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."}}