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VERSION:2.0
PRODID:-//Memento EPFL//
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SUMMARY:Learning Analytics for Adaptive and Self-Improving Learning Enviro
 nments
DTSTART:20170828T150000
DTEND:20170828T170000
DTSTAMP:20260407T091047Z
UID:a0e1f3723ee4119bc256670a971b4d16c4594d6b2d42625bc58db83c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Louis Faucon\nEDIC candidacy exam\nExam president: Prof. Volka
 n Cevher\nThesis advisor: Prof. Pierre Dillenbourg\nCo-examiner: Prof. Mar
 tin Jaggi\n\nAbstract\nToday\, Massive Open Online Courses attract million
 s of students worldwide. In particular\, EPFL MOOCs have reached more than
  a million participants. These large number reveal the hype of digital edu
 cation and brings new opportunities for educational research by giving it 
 the tools of the data sciences. For instance the implementation of large s
 cale peer grading in MOOCs shows the convergence between pedagogical metho
 ds and the digital revolution. This project explores these new opportuniti
 es for developing adaptive and self-improving learning environments.\n\nBa
 ckground papers\nPerformance Factor Analysis - A new Alternative to Knowle
 dge Tracing\, by Pavlik P.\, et al.\nFaster Teaching via POMDP Planning\, 
 by Rafferty A.\, et al.\nDeep Knowledge Tracing\, by PiechC.\, et al.
LOCATION:RLC D1 661 https://plan.epfl.ch/theme/generalite_thm_plan_public?
 lang=en&room=RLC%20D1%20661&dim_floor=1&dim_lang=en&baselayer_ref=grp_back
 grounds&tree_group_layers_centres_nevralgiques=information_epfl%2Cguiche
STATUS:CONFIRMED
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