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SUMMARY:Detecting Latent Training Needs Using Large Datasets
DTSTART:20180702T100000
DTEND:20180702T120000
DTSTAMP:20260409T175159Z
UID:c727378e3b0c202833510393068f97135196367eb79d0caaa3f2d5a4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Ramtin Yazdanian\nEDIC candidacy exam\nExam president: Prof. K
 arl Aberer\nThesis advisor: Prof. Pierre Dillenbourg\nThesis co-advisor: P
 rof. Robert West\nCo-examiner: Prof. Daniel Gatica-Perez\n\nAbstract\nTrai
 ning Need Analysis (TNA) is a field in management\, concerned with detecti
 ng\, understanding and subsequently addressing the learning needs of indiv
 iduals\, organisations\, and entire professions. The traditional processes
  used for TNA are slow and run the risk of falling behind rapidly changing
  training needs. In this document\, we present our research plan for addre
 ssing TNA - with a focus on the profession level - using large\, existing 
 and continually updated datasets. We believe that our methods should be ca
 pable of significantly speeding up the existing processes by providing var
 ious informative summaries of relevant data sources to human decision make
 rs\, who have the final say in the strategic decisions regarding the creat
 ion of training programs.\n\nBackground papers\nWhat are mobile developers
  asking about? A large scale study using stack overflow\, by Rosen\, C.\, 
 Shihab\, E.\nTraining needs analysis. A literature review and reappraisal\
 , by Gould\, D.\, et al.\nMachine Beats Experts: Automatic Discovery of Sk
 ill Models for Data-Driven Online Course Refinement\, by Matsuda\, B.\, et
  al.
LOCATION:RLC D1 661 https://plan.epfl.ch/?room==RLC%20D1%20661
STATUS:CONFIRMED
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