@article{VakkariVoelskePotthastetal.,
  author    = {Vakkari, Pertti and V{\"o}lske, Michael and Potthast, Martin and Hagen, Matthias and Stein, Benno},
  title     = {Predicting essay quality from search and writing behavior},
  series = {Journal of Association for Information Science and Technology},
  volume    = {2021},
  journal   = {Journal of Association for Information Science and Technology},
  number    = {volume 72, issue 7},
  publisher = {Wiley},
  address   = {Hoboken, NJ},
  doi       = {10.1002/asi.24451},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210804-44692},
  pages     = {839 -- 852},
  abstract  = {Few studies have investigated how search behavior affects complex writing tasks. We analyze a dataset of 150 long essays whose authors searched the ClueWeb09 corpus for source material, while all querying, clicking, and writing activity was meticulously recorded. We model the effect of search and writing behavior on essay quality using path analysis. Since the boil-down and build-up writing strategies identified in previous research have been found to affect search behavior, we model each writing strategy separately. Our analysis shows that the search process contributes significantly to essay quality through both direct and mediated effects, while the author's writing strategy moderates this relationship. Our models explain 25-35\% of the variation in essay quality through rather simple search and writing process characteristics alone, a fact that has implications on how search engines could personalize result pages for writing tasks. Authors' writing strategies and associated searching patterns differ, producing differences in essay quality. In a nutshell: essay quality improves if search and writing strategies harmonize—build-up writers benefit from focused, in-depth querying, while boil-down writers fare better with a broader and shallower querying strategy.},
  subject      = {Information Retrieval},
  language  = {en}
}