{"id":"W3157682407","doi":"10.14742/ascilite2020.0102","title":"Content analytics for curriculum review: A learning analytics use case for exploration of learner context","year":2020,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Curriculum; Thematic analysis; Computer science; Readability; Context (archaeology); Analytics; Content analysis; Learning analytics; Data science; Descriptive statistics; Mathematics education; Pedagogy; Psychology; Qualitative research; Sociology","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":[0.02660381,0.001022465,0.0009059669,0.005561118,0.002818665,0.007497896,0.002748115,0.003061739,0.002456121],"category_scores_gemma":[0.07137737,0.0007625641,0.001190723,0.003899947,0.002287754,0.008630696,0.007738825,0.002867891,0.001754038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00260852,"about_ca_system_score_gemma":0.003404683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002284898,"about_ca_topic_score_gemma":0.004623737,"domain_scores_codex":[0.9645523,0.02532976,0.001458947,0.001496701,0.006077654,0.001084639],"domain_scores_gemma":[0.8790736,0.0922354,0.003842281,0.01283081,0.009292146,0.002725786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009764628,0.001381853,0.04470514,0.002895173,0.000225209,0.01370291,0.1982722,0.004493601,0.03175153,0.0190372,0.02974543,0.6528133],"study_design_scores_gemma":[0.0005222699,0.002342862,0.03000863,0.00377809,0.0004596453,0.0163906,0.1082581,0.07588443,0.1043096,0.04137741,0.6161041,0.0005642842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5762837,0.002831622,0.349992,0.0221558,0.0004247264,0.003317547,0.002052092,0.009369058,0.03357343],"genre_scores_gemma":[0.5967566,0.001128521,0.3876339,0.001717954,0.0002565816,0.001599302,0.0009420661,0.001938146,0.008026913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02660381,"threshold_uncertainty_score":0.1406961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1819574636965769,"score_gpt":0.3343608705783562,"score_spread":0.1524034068817793,"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."}}