{"id":"W6983184465","doi":"","title":"LPR: an adaptive learning path recommendation system using ACO and meaningful learning theory","year":2017,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Path (computing); Cluster analysis; Component (thermodynamics); Recommender system; Personalized learning; Proactive learning; Unsupervised learning; Robot learning; Adaptive learning; Preference learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005758833,0.0001826495,0.0002603873,0.0002515058,0.001190212,0.000082435,0.0006535012,0.0002659496,0.000003713057],"category_scores_gemma":[0.00004142174,0.0002406376,0.00005607576,0.0001059947,0.00007060973,0.0008280403,0.0001455529,0.0005790658,0.000006758227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001898238,"about_ca_system_score_gemma":0.0001627459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004413407,"about_ca_topic_score_gemma":0.004922803,"domain_scores_codex":[0.9987878,0.0002977531,0.00009652607,0.0004453654,0.0001951542,0.0001774566],"domain_scores_gemma":[0.9986809,0.00009498379,0.0006711287,0.0002956893,0.0001963147,0.0000609247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006320629,0.0004195639,0.06450963,0.001232954,0.0008538005,0.0001467959,0.02393289,0.001982318,0.002548984,0.6467156,0.0004098328,0.2566155],"study_design_scores_gemma":[0.0008840121,0.001091289,0.1824289,0.001665861,0.000324056,0.00004679633,0.6703063,0.1341642,0.0004268094,0.002937891,0.004642738,0.001081155],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8688933,0.0001327483,0.1165419,0.0003606549,0.0007042738,0.0002659275,0.000004293662,0.0003236055,0.01277325],"genre_scores_gemma":[0.9811333,0.00006855771,0.01685383,0.000003869035,0.00004372872,5.55756e-7,0.0001170339,0.00001476364,0.001764385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6463735,"threshold_uncertainty_score":0.9812918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570561407901964,"score_gpt":0.2549670096933246,"score_spread":0.229261395614305,"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."}}