Temporary Research Assistant

Worcester Polytechnic Institute•Worcester, MA

3d•Onsite

About The Position

The candidate will perform basic and applied research in math education and support the processing and analysis of educational data that is produced as students engage with math problems using a variety of technology tools (eye tracking, executive function tasks, math technology). The researcher would join an interdisciplinary team to work on several NSF funded projects examining the intersections of math and algebra problem solving, math cognition, perceptual learning, student errors and strategy use, feedback, executive function and attention, and learning technologies. These projects will provide a lot of opportunities to examine student math performance and achievement outcomes as well as action and problem log-level data analysis of student interactions within technologies. The position is funded for one summer.

Requirements

Masters in Learning Sciences or a closely related field.
Proven publication track record and relevant research experience in some combination of math cognition, machine learning, educational data mining, learning analytics, classification algorithms, behavior detectors, or artificial intelligence is preferred.
Ability to communicate effectively (both verbally and written).
Ability to learn from and collaborate with researchers from a variety of interdisciplinary areas (math education, cognitive psychology, statistics).

Nice To Haves

Scholarly work involving empirical and theoretical studies of the cognitive and affective processes and mechanisms that underlie technology-enhanced learning.

Responsibilities

Work with an interdisciplinary team in the Learning Sciences and Technologies program that conducts research in K12 STEM education.
Utilize a variety of quantitative methods (ie. ML, clustering, multilevel modeling) to analyze data and develop data models to produce algorithms that identify student behaviors and mathematical strategies that could also predict learning and engagement.
Leverage rich data to understand phenomena related to learning, engagement, and performance.