{"id":"W2033973871","doi":"10.1007/s11528-014-0735-8","title":"Use of Lecture Capture in Higher Education - Lessons from the Trenches","year":2014,"lang":"en","type":"article","venue":"TechTrends","topic":"Innovations in Educational Methods","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Variety (cybernetics); Popularity; Computer science; Context (archaeology); Educational technology; Higher education; Automatic identification and data capture; Multimedia; Data science; Mathematics education; Psychology; Artificial intelligence; Political science","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":[0.003529558,0.000332615,0.0004309614,0.001613832,0.001569613,0.003352829,0.001160883,0.0009776731,0.01410712],"category_scores_gemma":[0.01625571,0.0003836992,0.0004165276,0.001337413,0.0007915319,0.002103149,0.00504532,0.001386258,0.002024052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002676052,"about_ca_system_score_gemma":0.002745312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005707787,"about_ca_topic_score_gemma":0.01800479,"domain_scores_codex":[0.9955692,0.001952665,0.0001482378,0.0006657498,0.001125869,0.0005382256],"domain_scores_gemma":[0.9900109,0.006591929,0.0004722007,0.001215578,0.0006848872,0.001024544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001171004,0.001485995,0.08775111,0.0007233074,0.00007534787,0.00042672,0.09277519,0.00169365,0.01001073,0.008426582,0.007156924,0.7883034],"study_design_scores_gemma":[0.0002553728,0.005487433,0.6075965,0.001944632,0.0002975615,0.001777629,0.114619,0.00757573,0.0372061,0.01028131,0.2126881,0.0002706431],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9150054,0.0007761221,0.008786803,0.001198574,0.0001189466,0.0003339698,0.0006278722,0.0004824847,0.07266985],"genre_scores_gemma":[0.9785678,0.0003186692,0.003699066,0.0002311127,0.00003537775,0.00008636792,0.0002801688,0.00007950207,0.01670202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01410712,"threshold_uncertainty_score":0.04719299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1579205868864687,"score_gpt":0.427168004029434,"score_spread":0.2692474171429653,"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."}}