Publikationen (FIS)

Predicting Knowledge Gain During Web Search Based on Multimedia Resource Consumption

authored by
Christian Otto, Ran Yu, Georg Pardi, Johannes Von Hoyer, Markus Rokicki, Anett Hoppe, Peter Holtz, Yvonne Kammerer, Stefan Dietze, Ralph Ewerth

In informal learning scenarios the popularity of multimedia content, such as video tutorials or lectures, has significantly increased. Yet, the users’ interactions, navigation behavior, and consequently learning outcome, have not been researched extensively. Related work in this field, also called search as learning, has focused on behavioral or text resource features to predict learning outcome and knowledge gain. In this paper, we investigate whether we can exploit features representing multimedia resource consumption to predict the knowledge gain (KG) during Web search from in-session data, that is without prior knowledge about the learner. For this purpose, we suggest a set of multimedia features related to image and video consumption. Our feature extraction is evaluated in a lab study with 113 participants where we collected data for a given search as learning task on the formation of thunderstorms and lightning. We automatically analyze the monitored log data and utilize state-of-the-art computer vision methods to extract features about the seen multimedia resources. Experimental results demonstrate that multimedia features can improve KG prediction. Finally, we provide an analysis on feature importance (text and multimedia) for KG prediction.

L3S Research Centre
External Organisation(s)
German National Library of Science and Technology (TIB)
GESIS - Leibniz Institute for the Social Sciences
Leibniz-Institut für Wissensmedien (IWM)
Conference contribution
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Theoretical Computer Science, Computer Science(all)
Electronic version(s) (Access: Open) (Access: Closed)