{"id":"W3149991423","doi":"10.82308/54040","title":"Machine learning for end-users: exploring learning goals and pedagogical content knowledge","year":2019,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Content (measure theory); Active learning (machine learning); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.005126416,0.0003598078,0.0003073168,0.001572959,0.001228707,0.006236558,0.0009248243,0.00116819,0.002918492],"category_scores_gemma":[0.01886313,0.0002106951,0.000392276,0.0009364545,0.001639877,0.006366227,0.002757359,0.001077838,0.0008064559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002331691,"about_ca_system_score_gemma":0.00185315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903675,"about_ca_topic_score_gemma":0.003565667,"domain_scores_codex":[0.9959638,0.002746104,0.0001240109,0.000361987,0.0005021619,0.0003019205],"domain_scores_gemma":[0.9818302,0.01446548,0.0009874797,0.0005890222,0.00132478,0.0008030267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003830477,0.001574572,0.165958,0.001164143,0.00008838115,0.0006670053,0.4416255,0.004713052,0.003762263,0.04534836,0.00617725,0.3285384],"study_design_scores_gemma":[0.0001103173,0.0009288538,0.1296323,0.001468512,0.0001259906,0.0007394966,0.5766711,0.06970415,0.009161497,0.1266884,0.08456539,0.0002038573],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9227269,0.000432643,0.03786,0.002123646,0.00001717252,0.0002613795,0.0001952146,0.0001990768,0.03618392],"genre_scores_gemma":[0.9798052,0.0002156224,0.01629857,0.0001443282,0.000004699652,0.000203527,0.0001299539,0.00003328091,0.003164877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006236558,"threshold_uncertainty_score":0.02711141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1464209083959854,"score_gpt":0.3103503441106673,"score_spread":0.1639294357146819,"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."}}