The Cognitive Diagnostic – Computerized Adaptive Testing for AP Statistics (AP-CAT) project is a five-year project funded by the National Science Foundation. The primary goal of this project is to create a CD-CAT system designed specifically to help students taking AP statistics to learn and master the course material. Aside from simply preparing students for their AP exam at the end of the school year, this project will assess the utility of the CD-CAT to determine if it is effective in increasing student engagement in statistics.
For more information, you can visit the project’s page.
Intelligent Diagnostic Assessment Platform (i-DAP) for High School Statistics Education
The i-DAP includes a series of assignments developed by content experts that each measures a number of key learning attributes that are mapped to the “statistics and probability” standards in the Common Core State Standards (CCSS). Students will receive formative feedback on their mastery of these attributes after completing an assignment. The i-DAP will also provide personalized learning materials that directly address their deficiencies. Teachers will have access to all individual diagnostic report, and group-level reports. Through these features, the i-DAP will help improve student engagement with statistics and in turn improve student learning of statistics.
For more information, you can visit the project's page.
Computational Social Science Research Experience for Undergraduates (REU)
The Computational Social Science REU program is program where students will work collaboratively with expert mentors and select from a wide variety of computational social science projects at the University of Notre Dame. Computational social science as an approach to analyzing the social world is has been growing rapidly. An increasing number of social interactions are taking place in the virtual world, using social media, mobile phones, and other electronic means. The digital traces of such interactions and the greater availability and detail of CSS data sets (e.g. surveys, census data, historical records) yield and exponential growth in data available for analysis. New cyberinfrastructure tools and methodologies for data analytics are needed to capitalize on this resource and enhance American economic competitiveness. This REU training environment will develop multidisciplinary social scientists with the appropriate expertise to answer the computational social science data growth challenges and opportunities.
For more information, you can look at previous projects here.
Statistical Quality Control of Low-Stakes Assessment Data
This research project will develop statistical methods to monitor and control the quality of low-stakes assessment and questionnaire data. Many assessment tests and surveys given by researchers in the social and behavioral sciences are perceived as low stakes by participants, yet researchers rely heavily on such data to address their research questions. Data from low-stakes assessments may contain a substantial portion of inattentive responses, driven by carelessness or fatigue from participants, or sometimes from malicious attempts by survey-bots. The increasing popularity of online platforms for participant recruitment and data collection further exacerbates the problem. Through this project we will develop statistical methods to detect inattentive responses, benchmark the performance of these methods, and identify the best method for different types of inattentiveness.
For more information about this project, please see here.
Effects of Test Mode on Test-taking Experience and Performance Data
The primary aim of this project is to determine the extent to which test mode affects students’ actual performance and subjective test-taking experience. For test mode, this project will examine an adaptive compared with a non-adaptive computerized test of introductory statistics. The aim is to investigate the effect of test mode on cognitive and non-cognitive aspects of the test-taking experience, as well as actual test performance. Further, we will examine whether test mode effects differ on the basis of membership to certain population subgroups, which are historically underrepresented in STEM disciplines. Further work on test mode may inform better assessment practices that both reduce the impact of negative testing experiences on students, and further provide more accurate and unbiased performance assessments.
For information about participating in the study, you can visit the Participant Opportunities page.
R Shiny app to identify careless responders
The purpose of this application is to allow researchers to upload their data, view potential careless respondents in the data identified by statistical methods, and independently decide which types of careless respondents should be removed. The application can be viewed at the https://careless-statistics.shinyapps.io/carelessstatistics/.