{"id":"W4280632955","doi":"10.1109/educon52537.2022.9766578","title":"Student Success in Asynchronous STEM Education: measuring and identifying contributors to learner outcomes","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Global Engineering Education Conference (EDUCON)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Northern Digital (Canada)","funders":"","keywords":"Asynchronous communication; Computer science; Mathematics education; Data science; Medical education; Psychology; Medicine; Telecommunications","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":[0.004737048,0.000366143,0.0003107026,0.001780484,0.0004512112,0.002119638,0.0004766812,0.0004462871,0.002044646],"category_scores_gemma":[0.02705691,0.0001208364,0.000322629,0.001439723,0.0003597054,0.001117145,0.002034801,0.000806155,0.0007712598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520019,"about_ca_system_score_gemma":0.0005536352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00119806,"about_ca_topic_score_gemma":0.002342807,"domain_scores_codex":[0.9970015,0.001027545,0.0003205461,0.0003314235,0.0009663512,0.0003526247],"domain_scores_gemma":[0.9643565,0.01671581,0.008819169,0.001969399,0.004031719,0.004107244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007358197,0.0002528853,0.982439,0.00002014691,0.00002494002,0.00001693511,0.0005758136,0.0001637333,0.0002397798,0.00005197703,0.00008864954,0.0160524],"study_design_scores_gemma":[0.000002682538,0.0003562786,0.9963869,0.00001440549,0.00001680944,0.0000292302,0.0009497515,0.001012154,0.0007144863,0.00009360633,0.0004129435,0.00001079609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981444,0.00003516979,0.0006546333,0.00003438662,0.000004773268,0.00002029943,0.0001252581,0.00001537082,0.000965831],"genre_scores_gemma":[0.9988035,0.00002921312,0.0004596424,0.000007866174,0.000007176161,0.00003283749,0.0001731977,0.00000702363,0.0004795996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004737048,"threshold_uncertainty_score":0.02505219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643356975357363,"score_gpt":0.2916444982952014,"score_spread":0.2752109285416278,"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."}}