From Science Student to Scientist: Predictors and Outcomes of Heterogeneous Science Identity Trajectories in College

This 5-year longitudinal study investigates the development of science identity throughout college from an expectancy-value perspective. Specifically, heterogeneous developmental patterns of science identity across 4 years of college were examined using growth-mixture modeling. Gender, race/ethnicity, and competence beliefs (efficacy for science tasks, perceived competence in science) were modeled as antecedents, and participation in a science career after graduation was modeled as a distal outcome of these identity development trajectories. Three latent classes (High with Transitory Incline, Moderate-High and Stable, and Moderate-Low with Early Decline) were identified. Gender, race/ethnicity, and competence beliefs in the first year of college significantly predicted latent class membership. In addition, students in the two highest classes were significantly more likely to report being involved in science careers or science fields after college graduation than students in the Moderate-Low with Early Decline class

Understanding the Standard Deviation: What Makes it Larger or Smaller?

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation

Body Measures: Exploring Distributions and Graphs Using Cooperative Learning

Using cooperative learning methods, this lesson introduces distributions for univariate data, emphasizing how distributions help us visualize central tendencies and variability. Students collect real data on head circumference and hand span, then describe the distributions in terms of shape, center, and spread. The lesson moves from informal to more technically appropriate descriptions of distributions

Histogram Sorting Using Cooperative Learning

Using cooperative learning methods, this activity provides students with twenty-four histograms representing distributions with differing shapes and characteristics. By sorting the histograms into piles that seem to go together, and by describing those piles, students develop awareness of the different versions of particular shapes (e.g., different types of skewed distributions, or different types of normal distributions), and that not all histograms are easy to classify. Students also learn that there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.)

Histogram Sorting Using Cooperative Learning

This activity has two major goals: (1) to give students experience with a variety of histograms of data, and (2) to help them better recognize different shapes and characteristics. Too often students only see one or two perfect examples (e.g., normal, right skewed) and have a difficult time describing and classifying histograms of real data. This activity also helps students determine which characteristics can appear together (e.g., skewed and bimodal) and which cannot be used together to describe a distribution (e.g., skewed and symmetric). This activity may be used to help students better understand the relationship between descriptions of data sets and the graphs that could be created from these data sets