ITEST
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Grant: Predicting STEM career choice from computational indicators of student engagement within middle school mathematics classes
Details
National Science Foundation Grant Number=DRL-1031398 June 1,2011-14, for $711,609.
Investigators
- Principal Investigator: Ryan S.J.d. Baker, Worcester Polytechnic Institute
- Co-Principal Investigator: Neil T. Heffernan, Worcester Polytechnic Institute
Abstract
The following project proposal targets the Research component of the ITEST program. In the proposed project, we will utilize automated detectors of student disengagement within middle school mathematics to improve prediction of students’ choices of STEM careers, and to improve theory on the processes that lead a student to select and begin a STEM career. We will apply automated detectors of three forms of student disengagement to previously collected data from Mathematics ASSISTments, a web-based tutor for middle school mathematics with formative assessment capabilities, originally developed with funding from Co-PI Heffernan’s NSF CAREER grant. We will apply these detectors to a diverse sample of students from five school districts in Central Massachussetts (including both urban and suburban districts), to assess students’ degree of disengagement within middle-school mathematics, and then track our sample of students forward through high school and into the school-to-college and school-to-work transitions. We will collect survey measures of vocational interest, self-efficacy, and math attitudes in high school, and after students transition to college or work, we will determine whether their current jobs or college majors involve STEM. By integrating data collected in 2004-2006 with data collected during the project, we can realize a 9 year long longitudinal research project within the 3 years available within the ITEST grant program.
We will then use the data set to answer two broad research questions: First, how well can automated measures of student disengagement in middle school mathematics class predict a student’s later choice of STEM college majors and STEM careers? Second, how does disengagement with mathematics drive and interact with the psychological and motivational phenomena and processes known to predict career choice?
To realize this project, we combine PI Baker’s expertise in the use of automated behavior assessments to study important phenomena in motivation and learning, Co-PI Heffernan’s expertise in using Math ASSISTments to benefit student learning while providing key information to teachers and other school personnel, and an diverse Advisory Board including world experts in career development, vocational interest, tracking participants over time via online communities and mail, educational data mining, programs to broaden participation, and data-driven decision making.
Broader Impact. Our measures and predictive models will support teachers, guidance counselors, and school administrators in identifying at-risk students who are likely to benefit from interventions to increase their preparedness for STEM careers, or interventions designed to make students more aware of STEM careers, towards building STEM workforce capacity. By using an economically and ethnically diverse sample of students from multiple communities, we increase the probability that the predictive model produced in this project can be used to benefit a broad population of students.
Intellectual Merit. The measures and models produced in this project can become a tool for researchers to study the success of programs designed to increase students’ interest in STEM careers, potentially improving assessment of these programs’ success. In addition, this work has the potential to significantly improve the field’s understanding of how student engagement with mathematics drives later attitudes towards mathematics, vocational interest in STEM careers, preparation for STEM careers, and STEM self-efficacy – demonstrating the key role that early disengagement with mathematics plays in the eventual decision to follow a STEM career. As such, this project has the potential to make transformative contributions to theory in career development.
