{"id":"W3049368898","doi":"10.1007/s40593-020-00202-6","title":"Utilizing Game Analytics to Inform and Validate Digital Game-based Assessment with Evidence-centered Game Design: A Case Study","year":2020,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence in Education","topic":"Educational Games and Gamification","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Computer science; Key (lock); Learning analytics; Process (computing); Artificial intelligence; Machine learning; Task (project management); TRACE (psycholinguistics); Dreyfus model of skill acquisition; Human–computer interaction; Engineering","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":[0.01753524,0.0007722522,0.0004751943,0.00227564,0.002269726,0.005004405,0.002281746,0.002063411,0.001306609],"category_scores_gemma":[0.04523613,0.0004577542,0.000524144,0.001134144,0.002606529,0.003255874,0.003759735,0.001971887,0.0004998809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00289883,"about_ca_system_score_gemma":0.005021934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004240262,"about_ca_topic_score_gemma":0.008813853,"domain_scores_codex":[0.987135,0.008934909,0.0006518763,0.0007251799,0.001798152,0.0007547819],"domain_scores_gemma":[0.9636049,0.02557177,0.001748195,0.002925593,0.004563451,0.001585996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001554061,0.03341215,0.2587635,0.001332528,0.0002827113,0.01086335,0.236154,0.01300021,0.01590944,0.04474341,0.007017693,0.376967],"study_design_scores_gemma":[0.00150233,0.01150678,0.1520614,0.003207278,0.0005834228,0.01287253,0.3592619,0.227966,0.0407979,0.07673032,0.1128273,0.0006829861],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543283,0.0001033864,0.03201709,0.0008337884,0.00004004591,0.002040372,0.0001117622,0.0001091438,0.01041613],"genre_scores_gemma":[0.9437881,0.0001155127,0.05376024,0.0001496236,0.000006141857,0.0006224956,0.00008383998,0.00003548309,0.001438552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01753524,"threshold_uncertainty_score":0.0927363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2287080862633832,"score_gpt":0.4648052278734977,"score_spread":0.2360971416101145,"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."}}