{"id":"W2731228715","doi":"10.1007/978-3-319-63874-4_6","title":"Group Matching for Peer Mentorship in Small Groups","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Mentorship; Computer science; Group (periodic table); Matching (statistics); Constructive; Constructive criticism; Session (web analytics); Peer feedback; Theoretical computer science; Mathematics education; Criticism; World Wide Web; Psychology; Mathematics; Medical education; Process (computing)","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":[0.005861508,0.0006054683,0.001631537,0.001157124,0.002043793,0.001367315,0.004100924,0.00235419,0.03251282],"category_scores_gemma":[0.02067712,0.0005743522,0.001406056,0.001433868,0.001145177,0.003684553,0.004485903,0.001665326,0.005609122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008868657,"about_ca_system_score_gemma":0.001307424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008309887,"about_ca_topic_score_gemma":0.0009898037,"domain_scores_codex":[0.9957923,0.00211353,0.0001644799,0.0006630897,0.0007903972,0.0004761867],"domain_scores_gemma":[0.9853303,0.009727165,0.0005467918,0.002970252,0.0007607585,0.000664648],"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":[0.001417825,0.001414291,0.001890379,0.0004478412,0.0001633363,0.0001274967,0.0007513394,0.02981688,0.005997231,0.120093,0.01252696,0.8253535],"study_design_scores_gemma":[0.0006064498,0.0018889,0.003294167,0.0001508108,0.0002000067,0.0003330466,0.0007848762,0.5371985,0.01258552,0.4240424,0.0188137,0.0001016485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06724612,0.0005607529,0.9064209,0.0005313294,0.0004150397,0.001029897,0.0001309575,0.001488854,0.02217613],"genre_scores_gemma":[0.5401136,0.0002432895,0.4262466,0.0002195587,0.0002694643,0.00125397,0.0002716964,0.0003041799,0.03107758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03251282,"threshold_uncertainty_score":0.1087662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07730487193262699,"score_gpt":0.3691509854338579,"score_spread":0.291846113501231,"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."}}