Posted: Wednesday, February 7, 2018 10:02 PM
OverviewACT is a nonprofit organization helping people achieve educational and workplace success. Our programs are designed to boost lifelong learning in schools and workplaces around the world. Whether it's guiding students along their learning paths, enabling companies to develop their workforce, fostering parent, teacher, and counselor understanding of student progress, guiding job seekers toward career success, or informing policymakers about education and workforce issues. ACT is passionate about making a difference in all we do.Learn more about working at ACT at act.org!ResponsibilitiesModels for data from learning and assessment have significantly diverged from each other over the past few decades. This project will explore the development of methodology at the intersection of learning and assessment. This includes psychometric models and machine learning based approaches for assessment of complex skills such as collaborative problem solving and remediation/learning strategies in a broad array of learning environments, including but not limited educational games and simulations. Typical work-related activities (depending on intern's background) include: Theoretical work to understand relationship between models for learning (eg Bayesian Knowledge Tracing) and models for assessment (eg Item Response Theory). Methodology/algorithm development to expand models to account for large scale data. Models for educational data mining and analysis of student/learner activity process data. Analysis of longitudinal learning data and application of novel models. Conducting review of the literature on the topic. Participating in regular meetings with supervisor(s). Writing reports & delivering presentations.QualificationsApplications will be reviewed on a rolling basis. Applications are accepted until the position is filled.PhD candidate in field related to computational statistics. Psychometrics, Educational Measurement, Computer Science, Statistics, Biostatistics, or another similar field.Required: Strong background in mathematics/statistics including an understanding of Bayesian methodology (eg MCMC, Bayesian networks). Proficiency in some programming language (eg C/C++, R, python, Matlab, Julia). Strong communication skills.Desired: Background in educational measurement. Experience with machine learning approaches, strong coding skills.
• Location: Iowa City
• Post ID: 19795672 iowacity