{"id":"W2742396379","doi":"10.1109/icalt.2017.151","title":"Tracking Students’ Analytical Reasoning Using Visual Scan Paths","year":2017,"lang":"en","type":"article","venue":"","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tracking (education); Computer vision; Artificial intelligence; Eye tracking; Computer graphics (images); Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006856122,0.0005431139,0.0002338619,0.001447644,0.000168235,0.001020997,0.0004023431,0.0005687323,0.003208878],"category_scores_gemma":[0.01190828,0.0002143103,0.0001969165,0.0006254537,0.000225319,0.000891649,0.0005890027,0.0004024043,0.000703382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710171,"about_ca_system_score_gemma":0.0005649533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785486,"about_ca_topic_score_gemma":0.002541255,"domain_scores_codex":[0.999428,0.0002072777,0.00002959966,0.0001476591,0.000146954,0.00004064643],"domain_scores_gemma":[0.9958019,0.002443148,0.0007392322,0.0001977753,0.0005822025,0.0002358597],"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":[0.001108623,0.000943562,0.2569875,0.0006139591,0.0001275616,0.0002969678,0.008698725,0.0162785,0.1341706,0.002386027,0.001843453,0.5765446],"study_design_scores_gemma":[0.0002965075,0.004650542,0.5257437,0.0003547116,0.0001850769,0.001797551,0.007022045,0.321997,0.1070965,0.01300292,0.01757952,0.0002739003],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8946019,0.0001720415,0.1000268,0.0001152602,0.00001763888,0.000151427,0.0003262579,0.0007456668,0.003843084],"genre_scores_gemma":[0.9321918,0.0002297744,0.06540693,0.00003122713,0.000005278592,0.0001503559,0.0002649829,0.00006609787,0.001653446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003208878,"threshold_uncertainty_score":0.01073474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0535083265671079,"score_gpt":0.3898604476213282,"score_spread":0.3363521210542203,"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."}}