{"id":"W3045169110","doi":"10.35631/ijhpl.310008","title":"RESOLVING ARABIC-LANGUAGE TEXT READING ERRORS AMONG UNIVERSITY STUDENTS THROUGH PROJECT-BASED LEARNING (PBL)","year":2020,"lang":"en","type":"article","venue":"International Journal of Humanities Philosophy and Language","topic":"Arabic Language Education Studies","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"Pusat Penyelidikan dan Inovasi, Universiti Malaysia Sabah; Universiti Malaysia Sabah","keywords":"Reading (process); Curriculum; Mathematics education; Session (web analytics); Arabic; Foreign language; Computer science; Set (abstract data type); Semitic languages; Language acquisition; Pedagogy; Psychology; Linguistics; World Wide Web","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":[0.001835492,0.0003499315,0.0004632637,0.000535894,0.001097308,0.00157268,0.0007609678,0.0007976096,0.001490918],"category_scores_gemma":[0.01090061,0.0001977048,0.0002354019,0.0004179277,0.0006700424,0.001300601,0.001378428,0.0009812308,0.0005029617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003272622,"about_ca_system_score_gemma":0.001140522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005341751,"about_ca_topic_score_gemma":0.0008396803,"domain_scores_codex":[0.9982462,0.0008016529,0.0001326834,0.0001483269,0.0004436719,0.000227468],"domain_scores_gemma":[0.9954345,0.001670088,0.001336949,0.0002478121,0.0008421543,0.0004684466],"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":[0.000381784,0.004330993,0.2884991,0.0005565291,0.00003941032,0.003136414,0.3164487,0.0006125161,0.01987348,0.000590376,0.002017052,0.3635136],"study_design_scores_gemma":[0.00009708943,0.007243065,0.3478698,0.0006405731,0.0001310914,0.00645471,0.5909702,0.003596169,0.02289144,0.002561351,0.01738341,0.0001610657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987637,0.00007408086,0.000451762,0.0001016619,0.000004379368,0.00002700873,0.000004212381,0.000009581096,0.0005636444],"genre_scores_gemma":[0.997505,0.0002191565,0.0010705,0.00006234491,0.000005780968,0.00003215349,0.0000130966,0.000005031538,0.001086864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001835492,"threshold_uncertainty_score":0.009707153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04800809277524487,"score_gpt":0.3373854637520397,"score_spread":0.2893773709767948,"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."}}