{"id":"W2398005666","doi":"10.1186/s41039-016-0036-2","title":"Identifying middle school students’ challenges in computational thinking-based science learning","year":2016,"lang":"en","type":"article","venue":"Research and Practice in Technology Enhanced Learning","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Computational thinking; Parallels; Computer science; Curriculum; Mathematics education; Face (sociological concept); Science education; Learning sciences; Domain (mathematical analysis); Educational technology; Artificial intelligence; Pedagogy; Psychology; Engineering; Mathematics; Sociology","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":[0.003398443,0.0007233708,0.0008947111,0.001982335,0.004256895,0.008648106,0.001399906,0.002019426,0.003154213],"category_scores_gemma":[0.01100238,0.0005657576,0.000760986,0.001409874,0.002783051,0.003999896,0.005063363,0.003012232,0.0009715456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00208872,"about_ca_system_score_gemma":0.003260171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003054111,"about_ca_topic_score_gemma":0.006670701,"domain_scores_codex":[0.9964796,0.0005652988,0.0002783675,0.0006346827,0.001095413,0.0009467442],"domain_scores_gemma":[0.9914461,0.002014777,0.0015896,0.000493104,0.00185314,0.002603256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003880739,0.005667144,0.5558041,0.000454155,0.00007405467,0.002322919,0.2867616,0.001093186,0.01755231,0.007087174,0.004644523,0.1181507],"study_design_scores_gemma":[0.00006251625,0.002311061,0.3547822,0.000416187,0.0001083092,0.001908483,0.5671493,0.005618525,0.01688489,0.01666765,0.0337989,0.0002919339],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976311,0.0000621411,0.000644898,0.0003149724,0.00001033445,0.00002920416,0.00001834802,0.00002301294,0.001265962],"genre_scores_gemma":[0.9965113,0.0001041336,0.001371111,0.0001916003,0.000004108897,0.00005516472,0.00004718421,0.0000145959,0.001700823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008648106,"threshold_uncertainty_score":0.01797295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1227372190327289,"score_gpt":0.4285218578663217,"score_spread":0.3057846388335929,"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."}}