Predicting Knowledge Gain during Web Search based on Multimedia Resource Consumption

TL;DR

This study explores how multimedia resource consumption during web search can be used to predict knowledge gain, demonstrating that multimedia features enhance prediction accuracy in learning scenarios.

Contribution

The paper introduces novel multimedia features for predicting knowledge gain and evaluates their effectiveness using computer vision techniques in a web search learning context.

Findings

Multimedia features improve knowledge gain prediction accuracy.

Computer vision methods effectively extract multimedia resource features.

Multimedia features are significant predictors alongside text-based features.

Abstract

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.