[on behalf of Noboru Matsuda]
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Please distribute widely!
We are looking for undergraduate students for summer internship on two
projects. This is supported by NSF, Research Experience for Undergraduate
(REU) hence only applies to US Citizens and equivalent (green card holders).
(1) ======================================
Project Title: Studying the effect of tutor learning using SimStudent as a
teachable agent
http://www.simstudent.org/2015-summer
Project Description: The primary purpose of the SimStudent project is to
study cognitive and social theories of the effect of tutor learning, which
is an empirically well-known phenomenon that students learn when they teach
others. To achieve this goal, we conduct classroom (in vivo) experiments
to test specific hypotheses using two key technologies: APLUS and
SimStudent. APLUS is an online game-like learning environment in which
students learn how to solve algebra equations by teaching a synthetic peer
learner, called SimStudent. SimStudent is a machine-leaning agent that
learns cognitive skills interactively when being tutored while solving
problems.
We are looking for one or two undergraduate student interns (paid
positions). SimStudent and APLUS provide a rich research environment. For
internship, there will be opportunities to study topics in computer science
such like improving SimStudent’s learning mechanism, or issues in
human-computer interaction such like improving interactions between (human)
students and SimStudent. For each individual summer intern projects, we
will provide an independent research project based on the intern's interest
and experience. There are a number of potential projects that would be
suitable for a summer intern projects.
See http://www.simstudent.org/2015-summer for details
(2) =========================================
Project Title: Data-Driven Methods to Improve Student Learning from Online
Courses
http://www.nsf.gov/awardsearch/showAward?AWD_ID=1418244
Project Description: The primary purpose of the project, under which we
are looking for one or two summer intern(s), is to build an integrated
development environment (IDE), called IDEA, for online course designers and
developers (the “course authors” hereafter) to conduct evidence-based
iterative online course improvement. The IDEA system consists of (1) a data
analytic module that analyzes online course contents and students’ learning
interaction data, (2) a course contents authoring module that facilitates
the direct editing of course contents linked with feedback from the data
analytic module, and (3) a data communication module that connects the data
analytic and authoring modules.
The goal of the internship will be to pursue a research experience under
the direct supervision of faculty members at Carnegie Mellon University.
Intern projects can be done on any part of the currently ongoing system
development that we mentioned above—i.e., data-mining technology, back-end
data-analytic technology, or front-end user-interface. We will provide
individual interns an independent research project based on their interests
and experience. The potential projects include the following (but are not
limited to):
+ Automated Knowledge Component Discovery Project – Can latent skills (aka
knowledge components) be automatically discovered from online learning
interaction data? Do machine-discovered skills reflect students’ learning
more accurately than the ones defined by subject matter experts? The goal
of this project is to develop a data-mining technique—e.g.,
matrix-decomposition, text mining, Bayesian net, etc.—and apply it to
actual OLI data. The intern will learn machine-learning techniques and will
have significant programming experience by the end of the internship period.
+ Data Visualization Project – How best can we visualize the data to
provide meaningful and constructive feedback to course designers? The goal
of this project is to evaluate the effect of data visualization in the form
of constructive feedback for evidence-based course refinement. The intern
will learn design skills (e.g., storyboard and prototyping) and
visualization techniques in addition to the significant exposure to the
programming experience.
+ Front-end Prototyping Project – What kind of authoring facilities are
necessary for course developers to conduct evidence-based course
improvement, and how can those facilities be implemented? The goal of this
project is to make an initial prototype for the course contents authoring
module using the interview data from the previous contextual inquiry as the
basis of a design proposal. Knowledge and experience in human-centered
methods (e.g., storyboarding, paper-prototyping, low/mid/high-fi
prototyping, etc.) would be acquired through this internship work.
+ Evidence-based OLI Course Improvement Project – How can we automatically
provide evidence-based feedback for courseware improvement? One of the
challenges is to understand how to interpret data-analytics feedback to
make constructive feedback for course designers. The goal of this project
is to understand how best data-analytic feedback can be interpreted and
used for iterative course improvement. We will target an actual OLI Biology
course and conduct an exploratory study involving course improvement by
hand. The intern will learn research skills in instructional design and
evaluation.
Interested students should send a CV to Noboru Matsuda <
Noboru.Matsuda@xxxxxxxxxx>, the project PI.
