{"id":"W2345653044","doi":"","title":"Personalization in learning by knowledge engineering with didactic knowledge","year":2010,"lang":"en","type":"article","venue":"Common Library Network (Der Gemeinsame Bibliotheksverbund)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Athabasca University","keywords":"Storyboard; Personalization; Profiling (computer programming); Pluralistic walkthrough; Computer science; Curriculum; Process (computing); Data science; Human–computer interaction; Multimedia; World Wide Web; Usability; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003795799,0.0004127202,0.0004182218,0.001494956,0.0002457425,0.0008254856,0.00125946,0.000217508,0.0002054919],"category_scores_gemma":[0.00004768606,0.000383563,0.0001004979,0.008791612,0.00007997613,0.0025923,0.0004777091,0.001469771,0.0001041411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002815828,"about_ca_system_score_gemma":0.000168044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001197519,"about_ca_topic_score_gemma":0.00006569318,"domain_scores_codex":[0.9976794,0.0002066307,0.0004129942,0.0006672498,0.0002675137,0.0007662039],"domain_scores_gemma":[0.9984025,0.0004420402,0.0001745014,0.000678036,0.00005112243,0.0002517651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001155734,0.002308401,0.5889525,0.0006047577,0.0002959261,0.0001432767,0.005628925,0.1054497,0.001178139,0.1364587,0.1297835,0.02908061],"study_design_scores_gemma":[0.0006981155,0.0002225108,0.003724091,0.0002562874,0.00001643073,0.00002643423,0.00004221212,0.7895639,0.0001337822,0.0007757776,0.2039338,0.0006066268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4220841,0.02331773,0.4021858,0.01316435,0.004517647,0.001561782,0.00001524041,0.007624541,0.1255288],"genre_scores_gemma":[0.9733262,0.0003038734,0.01764026,0.0002740142,0.0007817472,0.00002197458,0.00005654444,0.0001267434,0.007468604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6841142,"threshold_uncertainty_score":0.9998617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004477658780137962,"score_gpt":0.2210985832927713,"score_spread":0.2166209245126333,"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."}}