{"id":"W2909279678","doi":"10.1145/3284869.3284879","title":"Extracting Learning Outcomes Using Machine Learning and White Space Analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Scope (computer science); Task (project management); Process (computing); Machine learning; Artificial intelligence; White spaces; Information retrieval; Space (punctuation); Information extraction; Data mining; Natural language processing; Programming language; Engineering","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":[0.001235413,0.001349917,0.0008785948,0.006876307,0.0004755843,0.001890366,0.0006675555,0.0006686434,0.003168314],"category_scores_gemma":[0.009812671,0.0001618585,0.0007489285,0.003670854,0.0004422733,0.001916147,0.001171059,0.0007582342,0.003059602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006223239,"about_ca_system_score_gemma":0.0007926233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354046,"about_ca_topic_score_gemma":0.001625479,"domain_scores_codex":[0.9987664,0.0002126538,0.0001533924,0.000279218,0.0004688117,0.0001195175],"domain_scores_gemma":[0.9946153,0.002485262,0.0008803465,0.0004563529,0.001397941,0.0001648247],"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":[0.0001944714,0.0003046439,0.02010518,0.0002504482,0.00006091895,0.0002226925,0.0002167035,0.01279325,0.007189513,0.002332497,0.004465356,0.9518643],"study_design_scores_gemma":[0.0001224597,0.000967147,0.07944424,0.0002583683,0.0002575395,0.0003771989,0.001456436,0.7155982,0.1096552,0.06794256,0.0237561,0.0001645487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1641959,0.0005465773,0.8122599,0.0005297041,0.0001205339,0.0003590964,0.003490214,0.01084559,0.007652366],"genre_scores_gemma":[0.6566144,0.0004018801,0.3304854,0.00007597271,0.000148636,0.0006184876,0.006156397,0.0004309279,0.005067846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006876307,"threshold_uncertainty_score":0.01059908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177990684920947,"score_gpt":0.3106003221688882,"score_spread":0.2928012536767935,"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."}}