{"id":"W4281970065","doi":"10.3389/frai.2022.921476","title":"Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments","year":2022,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Personalization; Artificial neural network; Machine learning; Process (computing); Deep learning; Convolutional neural network; Tracing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007208246,0.0006779837,0.0004348441,0.0003558595,0.0002440983,0.0005218188,0.001137553,0.001146859,0.00150144],"category_scores_gemma":[0.002143722,0.0003770735,0.000428859,0.0003143729,0.0003459156,0.001655258,0.001066327,0.001310113,0.000371512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006897542,"about_ca_system_score_gemma":0.000496187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005367686,"about_ca_topic_score_gemma":0.007503849,"domain_scores_codex":[0.9996798,0.0000744741,0.00001670562,0.0001091604,0.00006581581,0.00005412188],"domain_scores_gemma":[0.9995053,0.0002084777,0.00004894944,0.00009344584,0.0001019151,0.00004196873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000307914,0.0004633702,0.004561883,0.0001116363,0.0001409278,0.0003369403,0.0003539826,0.4402419,0.03453143,0.006362228,0.003877316,0.5087104],"study_design_scores_gemma":[0.000007899357,0.00006168208,0.0005208261,0.000007317648,0.00001950822,0.00003321443,0.00001103175,0.9897689,0.00558199,0.003191853,0.0007878814,0.000007899448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.121568,0.0005973367,0.8706136,0.0004149622,0.00007663218,0.00005776512,0.0000777997,0.003035396,0.003558507],"genre_scores_gemma":[0.9255373,0.0002165266,0.06990778,0.0002425947,0.00004231671,0.00005797058,0.0001016763,0.00005375451,0.003839962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005367686,"threshold_uncertainty_score":0.01067293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04789751506804728,"score_gpt":0.2913577898409785,"score_spread":0.2434602747729313,"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."}}