{"id":"W3116332981","doi":"10.5281/zenodo.4399685","title":"LogCF: Deep Collaborative Filtering with Process Data for Enhanced Learning Outcome Modeling","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpretability; Collaborative filtering; Computer science; Machine learning; Artificial intelligence; Outcome (game theory); Recommender system; Process (computing); Scalability; Deep learning; Item response theory; Scale (ratio); Big data; Data mining; Psychometrics; Psychology; Database","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":[0.006566346,0.001220061,0.001554974,0.00166574,0.0006536891,0.001582399,0.002924954,0.001932841,0.002488264],"category_scores_gemma":[0.01558583,0.000719366,0.001830322,0.001473105,0.0006624483,0.001999755,0.001971478,0.002848182,0.000818848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519524,"about_ca_system_score_gemma":0.002156354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02193322,"about_ca_topic_score_gemma":0.02420755,"domain_scores_codex":[0.9982509,0.0006733648,0.0001245358,0.0004310751,0.0003560534,0.0001640117],"domain_scores_gemma":[0.993058,0.004414333,0.0004462755,0.000962915,0.0009064368,0.0002120555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003649014,0.0005057764,0.01023289,0.000234556,0.0003258784,0.0001594708,0.0003163396,0.6573456,0.002627033,0.01254922,0.006191843,0.3091465],"study_design_scores_gemma":[0.000009971111,0.00002374216,0.0003257797,0.000007518425,0.000008918018,0.000009281041,0.000005168397,0.9950238,0.0003356429,0.003862394,0.00038098,0.000006710087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01026571,0.0001586396,0.9872013,0.0001862656,0.00003554971,0.00008167858,0.0003096359,0.001450335,0.0003108674],"genre_scores_gemma":[0.4578586,0.0003738694,0.5337964,0.0004746123,0.0001418475,0.0007522613,0.00336387,0.0002937241,0.002944815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02193322,"threshold_uncertainty_score":0.04361111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08020965905941523,"score_gpt":0.3052064581609409,"score_spread":0.2249967991015256,"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."}}