Browsing by Subject "Statistics Education"

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    Assessment Of Cognitive Transfer Outcomes For Students Of Introductory Statistics
    (2015-10) Beckman, Matthew
    This study chronicles the creation of an assessment tool that quantifies cognitive transfer outcomes for introductory statistics students. Literature suggested that outcomes associated with cognitive transfer are closely aligned with statistical thinking and are indicative of students’ ability to apply learning to novel scenarios beyond the classroom. No assessment tool had been developed and published for the purpose of measuring cognitive transfer outcomes among statistics students. The results of this study suggest that the Introductory Statistics Understanding and Discernment Outcomes (I-STUDIO) assessment tool may effectively serve this purpose. The assessment tool was developed according to a rigorous protocol of expert feedback and iterative piloting. Data were collected and analyzed from a nationwide sample of nearly 2,000 students attending a wide variety of post-secondary institutions, and the I-STUDIO instrument was found to measure both forward-reaching and backward-reaching high road transfer outcomes with good psychometric properties. Data analysis indicated high reliability and diverse validity evidence. This evidence included confirmatory factor analysis models with compelling alignment to the theoretical model and analysis of qualitative themes among expert feedback. Analysis of scoring consistency also showed strong inter-rater agreement. Although the sample size of the scored responses is somewhat small by convention for item response theory, a graded response model generally showed good item functioning. Furthermore, the data suggested that the I-STUDIO assessment estimated student ability with consistent precision across a wide range of above-average and below-average students. Teachers and researchers can use I-STUDIO for comparing outcomes of alternative curricula. Additionally, the I-STUDIO instrument can be used to measure the effect of curriculum changes designed to improve transfer outcomes. Furthermore, the instrument and scoring rubric were designed to accommodate diverse curricula for the purpose of refining course outcomes.
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    Student Experiences Navigating Data Analysis Tasks in Fifth Grade Science and Engineering Settings
    (2020-08) Glancy, Aran
    Preparing students to use and consume data both inside and outside of school is an important goal in mathematics, science, and engineering education, but even basic data analysis tasks can quickly become complex. Planning and designing classroom data analysis tasks that support students’ learning of statistical principals requires an understanding of the ways that students engage with data as they work through authentic analysis tasks. In this study I investigate the obstacles and affordances that students encounter during such tasks, and I connect those obstacles and affordances to the characteristics of the task themselves. This study used a multiple, embedded case study approach (Yin, 2009). I observed eight groups of students from the classrooms of four different teachers as they worked through a variety of data analysis tasks in both engineering and scientific inquiry contexts. The teachers involved in this study were participants in the EngrTEAMS project, an MSP that supported teachers in the development and implementation of engineering-based STEM units. The two STEM units investigated in this study were developed by the teachers as part of the EngrTEAMS project. Audio and video recordings, student artifacts, and curriculum documents were the sources of data, and I analyzed these sources for themes among the obstacles and affordances encountered by the students. Students in this study encountered obstacles in all phases of the data analysis process. Measurement error, which came in many forms, was a primary obstacle for the students; however, the design of the tasks themselves also introduced challenges to the data analysis process. Students encountered difficulty when trying to coordinate or connect measurements, data representations, and the phenomenon they were investigating. Despite the obstacles they encountered, the hands-on experiences themselves, certain measurement instruments, and activities that encouraged students to make connections between representations helped support the students as they made sense of their data. The results suggest that engaging students in authentic data analysis tasks is complex work which presents both obstacles and affordance for students. Great care must be taken in planning these tasks to maximize the learning opportunities for students.