{"id":"W4385613436","doi":"10.20944/preprints202308.0366.v1","title":"Learning Analytics in the Era of Large Language Models","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Learning analytics; Interpretability; Personalization; Computer science; Usability; Analytics; Process (computing); Data science; Empowerment; Knowledge management; Artificial intelligence; World Wide Web; Human–computer interaction; Political science","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":[0.02209818,0.0009994826,0.001744374,0.003212138,0.001307008,0.01281982,0.00279953,0.002815499,0.005814361],"category_scores_gemma":[0.0831399,0.001303571,0.00151253,0.003235883,0.005698054,0.03026803,0.009467714,0.008480988,0.002993438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003293321,"about_ca_system_score_gemma":0.00296216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765938,"about_ca_topic_score_gemma":0.003045257,"domain_scores_codex":[0.9841875,0.009061602,0.0006822928,0.002014215,0.003624026,0.0004303544],"domain_scores_gemma":[0.8797406,0.09728654,0.002516399,0.01479922,0.004024347,0.001632905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002133133,0.0001238666,0.004281091,0.001403459,0.0002105532,0.0004848637,0.004059019,0.02721605,0.002605242,0.7299518,0.02327216,0.2061787],"study_design_scores_gemma":[0.00002223821,0.00004189339,0.0005775025,0.000330539,0.00002359751,0.0001779022,0.0005339587,0.08886233,0.0009342461,0.8387638,0.06967329,0.00005867432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01997749,0.01899739,0.8803828,0.05769674,0.0008809752,0.0001579303,0.00160433,0.005210353,0.01509186],"genre_scores_gemma":[0.3446782,0.01998022,0.6109377,0.007244724,0.003974539,0.0007102075,0.002851401,0.001700135,0.00792293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02209818,"threshold_uncertainty_score":0.1168678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1090935120277306,"score_gpt":0.3731757682263504,"score_spread":0.2640822561986197,"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."}}