{"id":"W3023367671","doi":"10.5539/cis.v13n2p54","title":"Increasing Student Engagement with Personalized Emails","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Computer science; Usability; Adaptability; Adaptation (eye); Set (abstract data type); Cover (algebra); World Wide Web; Multimedia; Human–computer interaction; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002918986,0.0004050917,0.0005447642,0.0008044208,0.0003994345,0.001672073,0.000581928,0.0008590173,0.004536777],"category_scores_gemma":[0.02102542,0.0001358068,0.0003611628,0.0005558783,0.0001960678,0.001033829,0.001561676,0.0006274993,0.001245374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002365741,"about_ca_system_score_gemma":0.0002703211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001771619,"about_ca_topic_score_gemma":0.0003190478,"domain_scores_codex":[0.9966364,0.001872005,0.0001975584,0.0003277269,0.0006353637,0.0003308516],"domain_scores_gemma":[0.9815806,0.01239811,0.001700797,0.001704321,0.001217176,0.001399018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001101429,0.01084803,0.2337556,0.0006292283,0.0001278069,0.0002752632,0.01277186,0.002925223,0.02902147,0.0008814581,0.002634115,0.7050284],"study_design_scores_gemma":[0.0003361547,0.02086086,0.8802418,0.0002942918,0.0005053609,0.0007360795,0.01464321,0.0247588,0.03113966,0.003662085,0.02262568,0.0001960924],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909213,0.00005070526,0.004699228,0.0001225301,0.00001229925,0.0000942124,0.00002592824,0.0002946111,0.003779129],"genre_scores_gemma":[0.9909104,0.0000683044,0.006599138,0.00007165295,0.00003186736,0.0001135105,0.00005956338,0.00002782947,0.002117793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004536777,"threshold_uncertainty_score":0.01543725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2227347223496704,"score_gpt":0.414293765005026,"score_spread":0.1915590426553555,"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."}}