  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 of
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.
