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Using multiple linear regression (MLR) and binomial logistic regression (BLR), the study analyzed variables influencing 170 preservice teachers’ preparedness to pass the science core of the official EC-6 Texas Examinations for Educator Standards (TExES). A practice EC-6 exam was administered in pretest and post-test formats alongside a QualtricsTM survey, with BIOL 1082 final grades emerging as the strongest predictor.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nRuthanne Thompson   \nUniversity of North Texas  \nNaudin Alexis  \nUniversity of North Texas  \nPamela Esprivalo Harrell   \nUniversity of North Texas  \nABSTRACT  \nPredicting preservice teachers’ performance on their certification examination may meaningfully help Educators Preparation Programs (EPPs) to adapt and integrate learning frameworks that can improve their passing rates. This study used multiple linear regression (MLR) and binomial logistic regression (BLR) to explore potential variables that may impact the preparedness of 170 pre-service teachers to pass the science core of the official EC-6 Texas Examinations for Educator Standards (TExES) certification examination. The study was conducted by issuing a practice EC-6 TExES certification examination in a pretest and post-test manner during the semester that the participating cohort were enrolled in BIOL 1082, a mandatory science course EC-6 preservice teachers need to take prior to the official state EC-6 TExES certification exam. Additionally, the cohort took an online QualtricsTM survey that collected ex post facto and other demographics data. The independent variables explored in this study included: final grade in BIOL 1082, classification, transfer status, prior college science coursework, enrollment status, family’s college history, and current GPA. The dependent variable used was the post-test score on the practice EC-6 exam. The independent variable, grade in BIOL 1082 was revealed to be the single best predictor of preservice teachers’ performance on the science practice examination across both the MLR and BLR models. The BLR models had a higher prediction accuracy of preservice teachers who would most likely fail the practice test than those who may pass at a prediction rate at approximately 79% accuracy. Based on the 67 out of 170 preservice teachers who passed the post-test, the accuracy of predicting failures may be a useful tool that EPPs can use in identifying students who may be at risk of failing and thus implement necessary interventions and other educational strategies.  \nKeywords: preservice teachers, multiple linear regression, binomial logistic regression, predictor factors, certification  \nIntroduction  \nScience and mathematics have long been considered the toughest subjects for students to master at the primary level through the tertiary level of education (Murphy & Smith, 2012; Pino-Pasternak &  \nVolet, 2018; van Aalderen-Smeets et al., 2017). The belief in preconceived poor performance in science have at times caused even the most brilliant of students to pivot to courses and degrees that were considered less challenging (Pino-Pasternak & Volet, 2018). The carried belief of mediocre performance in science at times has come from family members and for some students their low self-efficacy in science has come from their own teachers (Pino-Pasternak & Volet, 2018) . Further, according to Murphy et al.,(2007) many preservice teachers have themselves predicted that they will not perform well on the science portion of their certification examination. Additionally, some elementary classroom teachers, even after passing their certification examination, have expressed that they spend the least amount of time on the subject matter of science in the classroom (Binns et al., 2020) . This cyclical apprehension of preservice teachers’ ability to learn and teach science can in turn lead to their future students getting low exposure to science content, which can then be reflected as an ongoing lapse in students’ commitment to persevere in learning science. Later, if these same students pursue teacher certification, they may unintentionally relay to their students that success in science is unachievable (Binns et al., 2020; Pino-Pasternak & Volet, 2018) . Studies have also shown that a connection exists between the passing of the certification ex","cbCait8DyFS6A0AP","https://ap.wps.com/l/cbCait8DyFS6A0AP","pdf",581752,36,"English","# Abstract\n# Introduction","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To identify variables that influence preservice teachers’ preparedness to pass the EC-6 TExES science core certification exam and to support EPPs in improving passing rates.\"},{\"question\":\"Which method and data sources were used?\",\"answer\":\"The study applied multiple linear regression (MLR) and binomial logistic regression (BLR), using practice exam pretest/post-test scores and an online QualtricsTM survey for demographics and related information.\"},{\"question\":\"What factor best predicted preservice teachers’ practice exam performance?\",\"answer\":\"The final grade in BIOL 1082 was the single best predictor across both the MLR and BLR models.\"}]","Predicting Preservice Teachers' Performance on the Science Core of the EC-6 TExES General Certification Examination | PDF",1790706326,91]