{"id":"W2916593565","doi":"10.19173/irrodl.v20i1.3936","title":"The PERLA Framework: Blending Personalization and Learning Analytics","year":2019,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Learning analytics; Computer science; Analytics; Personalized learning; Data science; Human–computer interaction; World Wide Web; Teaching method; Open learning; Psychology; Mathematics education; Cooperative learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01094577,0.001520607,0.0009983197,0.003696623,0.001481514,0.01110833,0.002761018,0.002565422,0.003389975],"category_scores_gemma":[0.01438802,0.0008089827,0.001697336,0.003077427,0.006588009,0.01813145,0.008894894,0.00522544,0.002060938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002251399,"about_ca_system_score_gemma":0.00395708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001969617,"about_ca_topic_score_gemma":0.001673925,"domain_scores_codex":[0.9889747,0.005890256,0.0007296328,0.00193152,0.001946546,0.0005272537],"domain_scores_gemma":[0.9866022,0.00772657,0.0009869412,0.002477812,0.001249289,0.0009572835],"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":[0.00008503444,0.0002186117,0.002978035,0.001484452,0.00010649,0.0002310623,0.005672568,0.006421082,0.002093086,0.7415239,0.00647861,0.232707],"study_design_scores_gemma":[0.00003407879,0.0001710674,0.002081892,0.001279298,0.0001213128,0.0008642128,0.002323872,0.04850883,0.003592982,0.74426,0.1966179,0.0001444326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005730242,0.003791787,0.9524187,0.007218076,0.0001859953,0.0004255752,0.0002790236,0.002212404,0.02773813],"genre_scores_gemma":[0.1959259,0.004760329,0.7867971,0.001746175,0.0003891794,0.0009679475,0.0005598227,0.0003572242,0.008496293],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01110833,"threshold_uncertainty_score":0.05788743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05488048632393266,"score_gpt":0.4264142503425326,"score_spread":0.3715337640185999,"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."}}