{"id":"W2886521854","doi":"10.19173/irrodl.v19i5.3493","title":"Making Sense of Learning Analytics Dashboards: A Technology Acceptance Perspective of 95 Teachers","year":2018,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Learning analytics; Analytics; Computer science; Software deployment; Educational technology; Perspective (graphical); Instructional design; Software analytics; Knowledge management; Business analytics; Data science; Psychology; Mathematics education; Multimedia; Artificial intelligence; Software; Software development","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.007963295,0.0003836892,0.0007537323,0.002154465,0.003477355,0.009635613,0.0008496649,0.001447045,0.002879668],"category_scores_gemma":[0.02101414,0.0006543538,0.0005587765,0.001084654,0.004149484,0.003855893,0.004692356,0.002607303,0.0008201493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908175,"about_ca_system_score_gemma":0.002127925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006052634,"about_ca_topic_score_gemma":0.005513108,"domain_scores_codex":[0.9919327,0.002800063,0.0006235469,0.0007851544,0.002420041,0.001438565],"domain_scores_gemma":[0.9774469,0.01000226,0.003151858,0.0007077684,0.004716371,0.003974711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001116931,0.0004222252,0.1793483,0.0001697075,0.00002170438,0.001007468,0.7943602,0.00009170185,0.004082288,0.0008062433,0.0005318053,0.01904655],"study_design_scores_gemma":[0.00000930576,0.0003430797,0.05851774,0.0001127098,0.0000226451,0.0005499606,0.9326717,0.0002354544,0.0009497838,0.0002403689,0.006297959,0.00004927566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997238,0.000154837,0.0005164659,0.0003340948,0.000008560529,0.0000247582,0.00001564416,0.000008036222,0.001699454],"genre_scores_gemma":[0.9984169,0.0001789713,0.0002012196,0.0001155605,0.000004624912,0.00003234218,0.0000158207,0.00000815472,0.001026262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009635613,"threshold_uncertainty_score":0.04211444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09075648550091663,"score_gpt":0.4749030826457804,"score_spread":0.3841465971448637,"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."}}