Special topics on Technologies for Education
and Learning at Large Scale
|Soda Hall 405
The rapid development of computing technologies has greatly
transformed and will continue to transform the education landscape.
How will new technologies shape our education system in the future?
How can we best leverage new technologies to enhance the effectiveness
and the outreach of our education system, while reducing the cost?
What are the
key problems and challenges in achieving such goals?
How can our education system benefit from emerging technologies
such as crowd sourcing, pervasive computing, machine learning, and
What are the structural and societal implications of
these disruptive technologies
How will technology change the role of universities in the future?
In this course, we will explore these intriguing topics.
We will together
brainstorm, discuss, envision, and conceive the future education system.
We will also invent and build new technologies for education, and
collect real-world data to validate our designs.
Topics intended for discussion include but are not limited to:
-- Using program analysis and machine learning for automatic grading,
and automatic identification of students' problems
-- Personalized learning
-- Learning in a pervasive computing environment
-- New platform technologies for education
-- Game-based learning
-- Crowd-sourcing and learning at large-scale
-- Evaluation technologies
-- Economic and psychological aspects of technology-enhanced learning
-- Societal impact of online education
Please join Google Groups
to receive announcements about the class.
The class is research and discussion oriented.
Students will be asked to survey technologies and read related papers
We will invite guest lecturers to talk about selected topics.
Students will also be asked to give presentations on
selected technologies which they have surveyed.
Students should prepare to work on a class project.
Students should form groups of size 1 to 2 by the end of the 2nd lecture.
The project proposal is due at the end of the 4th lecture.
Students will give project presentations and submit a final project
report at the end of the semester.
40% Class particpation
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No prerequisite for graduate students,
although sufficient security background is expected. For undergraduate
students, please check with the instructor.
- Intended audience:
This course is intended for
graduate students who are interested in the intersection of
Both first year graduate
students and more senior graduate students are welcome.
We also welcome students with non-CS backgrounds who are
interested in the course, e.g., students majoring in psychology,
cognitive science, sociology, economics, education, policy,
or other backgrounds. Undergraduate students can be admitted to the class based on approval of the instructor.
The above information is subject to change.