{"id":"W3108972312","doi":"10.20429/ijsotl.2020.140208","title":"Using Prior Knowledge and Student Engagement to Understand Student Performance in an Undergraduate Learning-to-Learn Course","year":2020,"lang":"en","type":"article","venue":"International Journal for the Scholarship of Teaching and Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Student engagement; Psychology; Mathematics education; Multilevel model; Academic achievement; Medical education; Psychological intervention; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002562858,0.0004550306,0.0004580314,0.002173562,0.0006360239,0.002751739,0.0006056747,0.0007298865,0.002868532],"category_scores_gemma":[0.02915675,0.0002139793,0.0004137998,0.001128391,0.0005577522,0.002494717,0.001899886,0.001412118,0.0006734084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007838689,"about_ca_system_score_gemma":0.0007962939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003902743,"about_ca_topic_score_gemma":0.007376219,"domain_scores_codex":[0.9983054,0.0005444943,0.0001249592,0.0002303707,0.0005209448,0.0002738192],"domain_scores_gemma":[0.9800412,0.009435233,0.005483525,0.0009504705,0.001131702,0.002957816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001147713,0.00132943,0.9733421,0.00002633004,0.00007949171,0.00005856747,0.002166669,0.0006287536,0.0005129685,0.0002665544,0.0001642957,0.02131005],"study_design_scores_gemma":[0.000004491794,0.0003762361,0.992728,0.00004215927,0.00001989273,0.00005533256,0.002006172,0.002918214,0.0003137833,0.0009888198,0.0005268311,0.00002002891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996992,0.00007348174,0.0008829649,0.00008989572,0.000004117785,0.00001834381,0.00006315666,0.0000105638,0.001865411],"genre_scores_gemma":[0.9991801,0.00004219053,0.0003125212,0.00001579162,0.000005027483,0.0000129355,0.00007617463,0.000002595064,0.0003527058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003902743,"threshold_uncertainty_score":0.01355392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0860319926180235,"score_gpt":0.4183581297030646,"score_spread":0.3323261370850411,"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."}}