{"id":"W2295079971","doi":"10.1007/978-3-319-24489-1_23","title":"LewiSpace: An Educational Puzzle Game Combined with a Multimodal Machine Learning Environment","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Task (project management); Adaptation (eye); Human–computer interaction; Artificial intelligence; Game based learning; Educational game; Tracking (education); Eye tracking; Multimedia; Machine learning","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":[0.0001766279,0.001496549,0.0005002903,0.0005563365,0.0003732353,0.001184963,0.001638004,0.0008668842,0.02824226],"category_scores_gemma":[0.0007569559,0.0002973053,0.0005030773,0.0002370765,0.0003109554,0.001664074,0.002695572,0.000809034,0.00514115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003111763,"about_ca_system_score_gemma":0.0006200026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001992036,"about_ca_topic_score_gemma":0.00332493,"domain_scores_codex":[0.999851,0.00003809895,0.00000815822,0.00002844405,0.00004828511,0.00002611199],"domain_scores_gemma":[0.9998491,0.00004739276,0.000008136372,0.00001119354,0.00002095955,0.00006326533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004534343,0.003552705,0.003059316,0.001258037,0.0002636289,0.001514135,0.001465172,0.04214864,0.1283353,0.04643177,0.1019389,0.665498],"study_design_scores_gemma":[0.00170173,0.00418467,0.007189456,0.0003640789,0.0003311497,0.002307494,0.001041129,0.3703397,0.05760191,0.04146625,0.5130374,0.0004350641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1964066,0.0009088116,0.5943896,0.001126571,0.0005683465,0.00170357,0.002572388,0.04742935,0.1548948],"genre_scores_gemma":[0.5146124,0.0007475892,0.337097,0.0008877558,0.00008543691,0.001916074,0.003658595,0.001697215,0.1392979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02824226,"threshold_uncertainty_score":0.0944798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061919418007002,"score_gpt":0.2434390996705776,"score_spread":0.2228199054905076,"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."}}