Date of Graduation
The consequences of failure on the PPST Mathematics Test can be devastating for individuals in states requiring a passing score for teacher certification. This concern prompted the researcher to investigate the variables related to performance on the PPST Mathematics Test. The dependent measures were PPST Mathematics Test Raw Scores and Pass/Fail Scores. The demographic variables included Age, Sex, State, High School Mathematics Coursework, College Rank, and Teaching Area. The performance variables included High School GPA, ACT Mathematics Test, Nelson-Denny Reading scores, WVU Mathematics Department Computation and Word Problem scores, and Watson-Glaser Critical Thinking Appraisal scores. Subjects were 164 teacher education candidates attending West Virginia University. Four types of analyses were performed. One-way analyses of variance were performed using the Raw Score as the dependent variable. Chi-square computations were performed using the Pass/Fail Score as the dependent variable. The demographic variables of sex, Teaching Area, and High School Mathematics, and all performance variables were significant in relation to the dependent measures. From a stepwise regression analysis the best model derived for predicting from the Raw Score included: Sex, State, ACT Mathematics, Reading Vocabulary, Computation, Word Problem, and Critical Thinking-Interpretation scores. The best model derived from the Pass/Fail Score included: Computation, ACT Mathematics, and Word Problem pass/fail scores. In discriminant analyses, the best model to predict the Pass/Fail Scores included only two variables: Computation Score and ACT Mathematics Score. The hit-rate using this model was 84.7%. The most significant variable found across all analyses was the Computation Score. The findings suggest that teacher educators use the ACT Mathematics Score and the Computation Score to identify students at risk of failing the PPST, and then provide remediation focusing on the computation skills for those students. For students who do not pass the PPST Mathematics Test but appear to have adequate mathematics skills, teacher educators should assess those students on the performance variables included in the regression model on the Raw Score and focus remediation on any deficient skill. Replication of the study with other prospective teacher populations and further exploration of these relationships were recommended.
Digman, Sally Hardwick, "Student demographic and performance variables related to performance on the Pre-Professional Skills Mathematics Test." (1990). Graduate Theses, Dissertations, and Problem Reports. 8755.