{"id":"W4409764947","doi":"10.1007/978-3-031-86493-3_19","title":"Towards a Mobile, Intelligent, Personalized and Adaptive E-learning System Considering Learners’ Context in Semi-nomadic and Conflict Zones","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Université TÉLUQ","funders":"","keywords":"Computer science; Context (archaeology); Personalized learning; Human–computer interaction; Multimedia; Psychology; Mathematics education; Geography; Teaching method; Cooperative learning; Open learning; Archaeology","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":[0.0005068122,0.0002747694,0.0004415729,0.0003405351,0.0006011222,0.0002743245,0.0008494483,0.0001967233,4.716853e-7],"category_scores_gemma":[0.0001052892,0.0002449037,0.0000895052,0.0002298298,0.0005496196,0.0003121539,0.0008071325,0.0005803684,2.027041e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001161305,"about_ca_system_score_gemma":0.0002450215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001244601,"about_ca_topic_score_gemma":0.00005787403,"domain_scores_codex":[0.9987766,0.00003538599,0.0005642054,0.0002287284,0.0001827021,0.0002124424],"domain_scores_gemma":[0.9984887,0.0005832681,0.0004106665,0.0003379594,0.0001292203,0.00005023319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004018854,0.00001128412,0.00005863442,0.0006837894,0.0001004568,2.977917e-7,0.01895487,0.622756,0.000004151545,0.2629611,0.00001436734,0.094451],"study_design_scores_gemma":[0.0002473276,0.00008803037,0.0000725659,0.0009233045,0.00003302523,0.00002886247,0.0002706042,0.9831371,0.00001988723,0.0002893368,0.01462148,0.0002684574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001745993,0.003083677,0.991794,0.000397278,0.0003864084,0.0008029598,0.00001137572,0.00008381013,0.001694512],"genre_scores_gemma":[0.2956983,0.0006054676,0.7032151,0.0001211691,0.00006662895,0.00009650865,0.00001340065,0.00001788681,0.0001654581],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3603811,"threshold_uncertainty_score":0.9986884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408286470663287,"score_gpt":0.2502261168152748,"score_spread":0.226143252108642,"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."}}