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SUMMARY:Data Driven Student Feedback For MOOCs: Global Scale Education for
  the 21st century - Jonathan Huang\, Stanford University
DTSTART:20140318T110000Z
DTEND:20140318T120000Z
UID:TALK51587@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:In recent years an increasing number of students have turned t
 o online resources\, such as massive open online courses (MOOCs) for learn
 ing.  But while these online courses give teachers more coverage\, student
 -teacher ratios can often be ten thousand to one or worse. With such ratio
 s\, students no longer get the type of feedback they need to really unders
 tand the material.\nCodewebs is a system that I have been developing which
  addresses the problem of scalability in providing student feedback for on
 line programming-intensive courses.  Codewebs analyzes a massive code corp
 ora of historical student submissions and uses it to provide instant\, use
 ful and detailed student feedback to tens of thousands of students in the 
 same course.  By relying on a statistical approach\, the quality of feedba
 ck increases as our system sees more data and the feedback is automaticall
 y tailored for each assignment.  I will present a novel data driven techni
 que to discover shared "parts" amongst multiple student submission\, a pro
 blem that is complicated by the fact that there are always many ways to ac
 complish the same functionality in code. Throughout\, I will demonstrate r
 esults on Coursera's Machine Learning course\, which received over 1 milli
 on code submissions in its first run.\n\nFinally\, I will highlight the em
 erging issues of scalability and sustainability of education\, why these i
 ssues require insight from computer scientists and discuss specific proble
 ms in this domain that my future research program will address.\n
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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