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Learning Sciences and Technology- Leveraging The Learning Sciences & Technologies to Enhance Education and Learning in Secondary Schools


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Academic Level:
For most summer research programs, this is your upcoming status as of the fall. Always check with the individual program's website for details.

Undergraduates - First Year
Undergraduates - Sophomore
Undergraduates - Junior
Undergraduates - Senior

Note: this opportunity encourages applications from community college students.

Description:
This Research Experiences for Undergraduates (REU) Site at the Worcester Polytechnic Institute will provide a ten-week summer undergraduate research experience in learning sciences and technologies, with a focus on understanding achievement gaps among secondary school students. The increased use of educational technology has increased the amount of educational data available to researchers. These data contain knowledge that could be analyzed to enhance understanding of how students best learn and how best to teach them.

This REU will enable young researchers to analyze this data and contribute to understanding high-impact teaching practices, how to group learners for instruction, and what type of feedback or remediation is most applicable to a learner. These research activities will provide new information about the effectiveness of both technological and traditional educational interventions for a variety of learners in secondary school classrooms. Undergraduates will analyze data using educational data mining techniques, such as student modeling to better estimate intervention impacts and clustering to determine which students are most similar. Participating students will enjoy in person, larger group activities with the REU site hosted by Dr. Rundensteiner in Data Science.


Application Deadline: 2/1/2023

Participating Institution(s):
(Click an institution to see all programs it hosts)
Worcester Polytechnic Institute (Lead)

Program Materials:
 • Program Website 
movie  Program Video

This Program can be Described by:
Academic Disciplines:
Computational Sciences
Computer Sciences
Education
Statistics
STEM Fields

Keywords:
Computational Statistics
Educational Technology
Machine Vision & Learning
Quality of Life Technology
Scientific Computing

Learn More and Apply!

This program is funded by:
National Science Foundation (NSF)


Page last updated 11/9/2022
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