• Press Information
  • Contact
  • deutsch | english
mobile icon
Project

SALIENT: Search as Learning – Investigating, Enhancing and Predicting Learning during Multimodal Web Search

WorkgroupMultimodal Interaction
Knowledge Construction
Duration05/2018 - 04/2021
FundingLeibniz Association, funding line "Cooperative Excellence" of the 2018 Leibniz Competition
Project description

The Internet has become indispensable when it comes to searching for information. Such an information search can be understood as a self-regulated learning process: Constructing knowledge while surfing the seemingly endless data stream is a challenging task. The SALIENT project contributes to a better understanding of search as learning and develops methods to support the acquisition of knowledge through the Internet with the help of ranking and retrieval algorithms.


Established information retrieval approaches address the relevance of search results for an information need, whereas the actual learning scope of a user is usually disregarded. The SALIENT project aims at closing this gap by developing a theoretical framework for describing information search on the Internet as search as learning. In cooperation with the Leibniz Information Centre for Science and Technology and University Library (TIB), the L3S Research Center, and GESIS cologne, the project aims at developing methods for predicting learner intents and knowledge states from user behavior during Internet search. Furthermore, these methods are to be used to support Internet users in their knowledge acquisition. Learning with multimodal resources and acquisition of procedural knowledge is one key area of research. Previous work by project members was able to identify a number of specific features for predicting the knowledge state and knowledge gain of learners searching the web (Yu, Gadiraju, Holtz, Rokicki, Kemkes, Dietze, 2018).

Cooperations
  • Leibniz Information Centre for Science and Technology and University Library (TIB)

  • L3S Research Center

  • Leibniz-Institut für Sozialwissenschaft GESIS

Publications
  • Ran Yu, Ujwal Gadiraju, Peter Holtz, Markus Rokicki, Philipp Kemkes and Stefan Dietze, 2018. Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM SIGIR 2018. https://doi.org/10.1145/3176349.3176381

Website

Project Website: SALIENT


Project logo