{"id":"W2096591577","doi":"10.1504/ijlt.2013.059130","title":"Ecological content sequencing: from simulated students to an effective user study","year":2013,"lang":"en","type":"article","venue":"International Journal of Learning Technology","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Similarity (geometry); Object (grammar); Selection (genetic algorithm); Learning object; Value (mathematics); Human–computer interaction; Artificial intelligence; Multimedia; Machine learning; Image (mathematics)","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":[0.00063035,0.0001594438,0.0003161391,0.0006109958,0.00009340895,0.0003078294,0.002374089,0.0001270289,0.00006678486],"category_scores_gemma":[0.0008177877,0.0001276986,0.00007906165,0.0002511529,0.00002898742,0.0005409083,0.0005644952,0.000766473,0.0001836543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003577535,"about_ca_system_score_gemma":0.00003970411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003145827,"about_ca_topic_score_gemma":0.000009744394,"domain_scores_codex":[0.9979051,0.0002998892,0.0005473249,0.000314296,0.0007010876,0.0002322895],"domain_scores_gemma":[0.9976377,0.0002400222,0.000455641,0.0002142133,0.001339542,0.0001128208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006196467,0.0008340726,0.8811061,0.000001265595,0.0007242991,0.0007341311,0.003846619,0.04815863,0.02460011,0.00873863,0.00006130821,0.03113282],"study_design_scores_gemma":[0.003671133,0.0135954,0.9200387,0.0003453055,0.00004890067,0.0004114815,0.01429781,0.01898106,0.008268589,0.004130983,0.01542864,0.0007820203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8844788,0.0000187428,0.1131167,0.0008011703,0.00107475,0.0003364841,3.094999e-7,0.000140736,0.00003227207],"genre_scores_gemma":[0.9937952,0.000001541377,0.00536282,0.0001211869,0.0001968979,0.00001462077,6.120215e-7,0.00001172179,0.0004953685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1093164,"threshold_uncertainty_score":0.5207398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03402786176529475,"score_gpt":0.317115306104981,"score_spread":0.2830874443396862,"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."}}