{"id":"W4415686098","doi":"10.1007/978-3-031-95365-1_9","title":"The Use of Natural Language Processing in Learning Analytics","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Learning analytics; Context (archaeology); Information extraction; Sentiment analysis; Analytics; Natural language understanding; Educational data mining","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":[0.0005661739,0.00007539558,0.0001698047,0.0001329213,0.000222472,0.0000818582,0.0001394617,0.00008054235,0.00009302617],"category_scores_gemma":[0.0004471046,0.00005149166,0.0001024407,0.0001354623,0.0001380949,0.00005787718,0.00004601746,0.0002734997,0.000002197095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004852356,"about_ca_system_score_gemma":0.0002229446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008472941,"about_ca_topic_score_gemma":0.009226836,"domain_scores_codex":[0.9992057,0.00008851982,0.0002121727,0.000114193,0.0002809083,0.00009852002],"domain_scores_gemma":[0.998852,0.0007942608,0.0001431966,0.00006084984,0.0001329681,0.00001675358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005291941,0.000004034023,0.000276654,0.00001715178,0.00003399835,0.000003761244,0.002661405,0.0007343581,0.000002036969,0.3116319,0.0003355607,0.6842938],"study_design_scores_gemma":[0.00008269621,0.000007284721,0.000305249,0.0002233175,0.00009847297,1.573324e-7,0.002192354,0.0393698,0.000002957991,0.01543052,0.9420869,0.0002002369],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001319552,0.002859131,0.001649274,0.000591608,0.00009623089,0.00009118535,0.000001057667,0.0000274386,0.9945521],"genre_scores_gemma":[0.03098976,0.0001455475,0.002509024,0.00003751247,0.00006199316,5.939415e-7,0.000005886767,0.000004312663,0.9662454],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9417514,"threshold_uncertainty_score":0.5148791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06797414726166208,"score_gpt":0.3814139389179367,"score_spread":0.3134397916562746,"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."}}