Description
The
PLUS - Personalized Learning
Squared project, run by the Human-Computer Interaction
Institute at Carnegie Mellon University aims to double the rate of
math learning in middle school students, particularly those who
have been historically underserved. This project is operated in
collaboration with Carnegie Learning and Stanford University and is
led by Principal Investigator Prof. Ken Koedinger and Research Lead
Dr. Danielle Thomas. PLUS features a hybrid tutoring platform that
combines human and AI tutoring to deliver personalized learning for
each student.
The
National
Tutoring Observatory , is a research infrastructure led by
Prof. Rene Kizilcec at Cornell University, along with researchers
from Carnegie Mellon University, Massachusetts Institute of
Technology, and educational strategy company FreshCognate. The
mission of the National Tutoring Observatory is to observe and
record great teachers and tutors at work in one-on-one and small
group interactions with learners. By partnering with a range of
tutoring providers, we will create the world's largest repository
of video and transcript data about tutoring interactions and the
incredibly important work of teachers. The Observatory aspires to
create a Million Tutor Moves dataset that records at least one
million interactions between teachers and students. These new data
will advance the science of instruction, provide important data for
technologists developing AI tools, and allow the Observatory to
create a pipeline for the creation of open-source AI tools.
About The Role:
We are looking for a postdoctoral fellow who will contribute to
original research to improve student learning outcomes through
improved tutoring for the PLUS project and the NTO. This fellow
would be advised by Prof. Ken Koedinger and Dr. Danielle Thomas at
Carnegie Mellon, while closely collaborating with Rene Kizilcec and
Co-PIs of The National Tutoring Observatory. . This includes work
on improving tutor training, improving tutor-student matching, and
developing AI-assisted support tools for tutors. This is an
excellent opportunity for a technology-oriented postdoc researcher
to make an impact on an exciting and fast-scaling project in the
areas of educational data science, learning science, and education
technology.
The ideal candidate will demonstrate strong problem-solving skills
and apply interesting research methods and procedures to answering
the following example research questions:
- What are the key drivers of effective teaching and tutoring,
and how do they vary across different tutoring modalities,
implementations, and student demographics?
How can we best measure and isolate the tutoring actions and
behaviors that correlate with, and ultimately predict, gains in
student learning and engagement?
How can AI-driven tutoring tools be used to improve human tutor
training and professional development?
How can AI-driven tutoring tools be used to improve the
learning experiences and outcomes for students?
The ideal candidate will also possess strong quantitative analysis
skills, and your responsibilities will include:
- Collaborative problem-solving toward innovation in tutoring
with different stakeholders
- Design and implement evaluations of innovative programs
- Data analyses and modeling using Python/R/or similar
programming languages
- Co-author research papers on the results of student learning
modeling and studies
- Attend required meetings and participate in various seminars
and training workshops to maintain or update skills
What We Offer:
- Competitive salary and benefits package including an automatic
8% monthly contribution to a retirement plan
- Professional development opportunities including tuition
benefits at CMU for you and your dependents
- Comprehensive health insurance and generous PTO
- Chance to work with the world’s foremost learning science
researchers
- A fun, high-octane team that will work with you every step of
the way
Salary : $60,000-$75,000
Qualifications
What You Bring:
- Ph.D. in Cognitive Science, Learning Science, Computer Science,
Information Systems, or related fields
- A record of publications in Cognitive Science, Learning
Science, AI in Education, and/or Educational Data Mining
(conferences such as AIED, LAK, EDM, etc.)
- Collaborative problem-solving skills to work effectively with
state assessment professionals toward innovation in
assessment
- Experience working with discourse analyses, such as audio
transcription analysis and multimodal data analysis
- Data analysis skills with R/Python/or similar programming
languages
- Experience in public education is a plus
Application Instructions
Applications should include a cover letter describing
qualifications and expressing interest, a CV and contact
information for 2 references and a published paper (optional).
Please direct questions and interest in the position to Danielle
Thomas:
drthomas@cmu.edu
.
Equal Employment Opportunity Statement
Carnegie Mellon University is an equal opportunity employer.
It does not discriminate in admission, employment, or
administration of its programs or activities on the basis of race,
color, national origin, sex, disability, age, sexual orientation,
gender identity, pregnancy or related condition, family status,
marital status, parental status, religion, ancestry, veteran
status, or genetic information. Furthermore, Carnegie Mellon
University does not discriminate and is required not to
discriminate in violation of federal, state, or local laws or
executive orders.