The Power of Demographic and High School Experience Factors on Geoscience Success

A growing body of literature documents that demographic and high school experience factors correlate with academic achievement in higher education. The most significant variables influencing entry-level college science success include gender, age, high school GPA, high school class, and socio-economic status variables (Hedges and Nowell, 1995; Caldas and Bankston, 1997; Sadler and Tai, 2007). Although this previous work suggests that non-cognitive variables can be used to predict success in some sciences, the geological sciences were overlooked in this prior work. This study aims to examine the relationship between demographic and high school experience factors and student understanding of geosciences, exposed by the Geoscience Concept Inventory (GCI; Libarkin and Anderson 2005). More than 2500 entry-level students from 38 colleges and universities located in 22 states of the United States completed a 19-item version of the GCI, coupled with a background form requesting gender, high school GPA, birth date, major, race, high school science experience, and highest degree of male and female parents. The results of the Pearson productmoment correlation revealed a small to medium size effect between the GCI scores and gender, high school GPA, race, female and male guardian education level, enrollment in high school physics, and institutional type. The multiple stepwise linear regression analysis indicated that gender, high school GPA, race, enrollment in high school physics, and institutional type appeared to be the best predictors for student success on the GCI. Overall, these predictors account for less than 20% of the fluctuation in GCI pre- or post-test scores, indicating that the GCI is a good measure of geoscience understanding across populations

Analysis test of understanding of vectors with the three-parameter logistic model of item response theory and item response curves technique

This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming unidimensionality and local independence. Moreover, all distractors of the TUV were analyzed from item response curves (IRC) that represent simplified IRT. Data were gathered on 2392 science and engineering freshmen, from three universities in Thailand. The results revealed IRT analysis to be useful in assessing the test since its item parameters are independent of the ability parameters. The IRT framework reveals item-level information, and indicates appropriate ability ranges for the test. Moreover, the IRC analysis can be used to assess the effectiveness of the test’s distractors. Both IRT and IRC approaches reveal test characteristics beyond those revealed by the classical analysis methods of tests. Test developers can apply these methods to diagnose and evaluate the features of items at various ability levels of test takers