{"id":"W4414464210","doi":"10.1109/imsa65733.2025.11166923","title":"Comparative Evaluation of Modular Deep Learning Pipelines for Student Engagement Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"","keywords":"Pipeline (software); Modular design; Adaptability; Pipeline transport; Deep learning; Binary classification; Binary number; Student engagement","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":[0.003256064,0.002500636,0.0008670627,0.001416516,0.0004873788,0.001078827,0.002752073,0.001521152,0.004664961],"category_scores_gemma":[0.007862819,0.0006192744,0.0008761737,0.0009614212,0.0005968044,0.00297454,0.002404981,0.001762892,0.002438801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440811,"about_ca_system_score_gemma":0.001593489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01043107,"about_ca_topic_score_gemma":0.01328703,"domain_scores_codex":[0.9985268,0.0003251744,0.0001037893,0.0004776155,0.0003092617,0.0002573566],"domain_scores_gemma":[0.9974975,0.001071995,0.000136757,0.00044877,0.000589926,0.0002550519],"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":[0.004688947,0.001728783,0.01916092,0.0009268107,0.0007147925,0.000305791,0.0002193382,0.1800705,0.01720085,0.002636825,0.03016443,0.742182],"study_design_scores_gemma":[0.0002737378,0.001075588,0.005382175,0.00006798728,0.0001357742,0.0001301893,0.0001130445,0.9688891,0.01775418,0.002017437,0.004115997,0.0000449132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7050316,0.004580964,0.2220347,0.001627699,0.0008004523,0.0009356862,0.005949552,0.04447867,0.01456061],"genre_scores_gemma":[0.839163,0.0009645809,0.1344724,0.0005446362,0.0001067158,0.0005014726,0.01631573,0.0006203916,0.007311136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01043107,"threshold_uncertainty_score":0.02074075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0579600997113324,"score_gpt":0.4033891854684084,"score_spread":0.345429085757076,"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."}}