{"id":"W4309941226","doi":"10.1186/s40561-022-00214-w","title":"Reuse of e-learning personalization components","year":2022,"lang":"en","type":"article","venue":"Smart Learning Environments","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Personalization; Reuse; Computer science; Interoperability; Adaptation (eye); Component (thermodynamics); Interface (matter); Software engineering; Set (abstract data type); Architecture; Human–computer interaction; World Wide Web; Programming language; Engineering; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006100456,0.0001376999,0.0001679378,0.0001444112,0.0007671167,0.00006157484,0.0009065973,0.00003402168,0.0008741226],"category_scores_gemma":[0.0001560213,0.0001639168,0.00006887018,0.0003128247,0.00004259986,0.000313139,0.001131361,0.0006353461,0.0001896349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548692,"about_ca_system_score_gemma":0.00003138471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003841093,"about_ca_topic_score_gemma":3.100292e-7,"domain_scores_codex":[0.9978213,0.0006177395,0.0002661391,0.0003872437,0.0006354945,0.0002720966],"domain_scores_gemma":[0.9991603,0.00007274908,0.0002865969,0.0003811161,0.00001119728,0.00008805992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004556335,0.0007389301,0.6498201,0.00005224907,0.0001096253,0.00001808361,0.025174,0.2689411,0.03926665,0.005337164,0.001747911,0.00874862],"study_design_scores_gemma":[0.0009328712,0.0004726261,0.2188814,0.0000275099,0.00001906695,0.00002352997,0.006672685,0.07456774,0.000603119,0.0001150566,0.6972191,0.0004653037],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576975,0.0001021222,0.02554511,0.0008868697,0.0008161525,0.0002648861,0.000001396522,0.0002131005,0.01447285],"genre_scores_gemma":[0.9847628,0.00002786992,0.002640996,0.0001031537,0.00002682776,0.00002882348,0.00006212726,0.00002328064,0.01232405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6954712,"threshold_uncertainty_score":0.957103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01642821875533802,"score_gpt":0.2283214697196858,"score_spread":0.2118932509643478,"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."}}