{"id":"W4412032557","doi":"10.20853/39-3-6383","title":"Can MS Excel help Finance students to Excel? A study in student work readiness","year":2025,"lang":"en","type":"article","venue":"South African Journal of Higher Education","topic":"Spreadsheets and End-User Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Ms excel; Higher education; Work (physics); Mathematics education; Student engagement; Computer science; Finance; Psychology; Business; Economics; Engineering; Software engineering; Economic growth","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":[0.003272614,0.0002682789,0.0003248726,0.0007233084,0.0009445556,0.00119866,0.0004779329,0.0007237018,0.004261335],"category_scores_gemma":[0.0104004,0.0002727463,0.0005647328,0.0006115481,0.0004515828,0.001247292,0.0007043738,0.001588559,0.001114281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003230236,"about_ca_system_score_gemma":0.001284157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000883822,"about_ca_topic_score_gemma":0.001682468,"domain_scores_codex":[0.9990477,0.0004272044,0.00006661237,0.00006200471,0.0001922278,0.0002042456],"domain_scores_gemma":[0.9942294,0.00278472,0.0006593136,0.0001671088,0.0007167262,0.001442719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003497504,0.08142059,0.5076336,0.0008392624,0.0002227706,0.001200801,0.06769564,0.0005328411,0.006010158,0.001186098,0.005198931,0.3245617],"study_design_scores_gemma":[0.0005896786,0.04749415,0.8098678,0.0004504178,0.0002240123,0.0005812872,0.1126012,0.001111781,0.00532159,0.0009471843,0.02070382,0.0001072363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998762,0.00007386311,0.00005164178,0.0002359291,0.000008882532,0.00004320766,0.00002068576,0.000002991707,0.0008007819],"genre_scores_gemma":[0.9975448,0.000267458,0.0002137814,0.0002672267,0.00001030211,0.00007801791,0.00003716413,0.000003423771,0.00157779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004261335,"threshold_uncertainty_score":0.0173074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656385374542807,"score_gpt":0.31992405448296,"score_spread":0.3033602007375319,"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."}}