Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality

Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed

Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features

Predicting the success of Conversational Task Assistants (CTA) can be critical to understand user behavior and act accordingly. In this paper, we propose TB-Rater, a Transformer model which combines conversational-flow features with user behavior features for predicting user ratings in a CTA scenario. In particular, we use real human-agent conversations and ratings collected in the Alexa TaskBot challenge, a novel multimodal and multi-turn conversational context. Our results show the advantages of modeling both the conversational-flow and behavioral aspects of the conversation in a single model for offline rating prediction. Additionally, an analysis of the CTA-specific behavioral features brings insights into this setting and can be used to bootstrap future systems

Spoken conversational search: speech-only interactive information retrieval

This research investigates a new interface paradigm for interactive information retrieval (IIR) which forces us to shift away from the classic "ten blue links" search engine results page. Instead we investigate how to present search results through a conversation over a speech-only communication channel where no screen is available. Accessing information via speech is becoming increasingly pervasive and is already important for people with a visual impairment. However, presenting search results over a speech-only communication channel is challenging due to cognitive limitations and the transient nature of audio. Studies have indicated that the implementation of speech recognizers and screen readers must be carefully designed and cannot simply be added to an existing system. Therefore the aim of this research is to develop a new interaction framework for effective and efficient IIR over a speech-only channel: a Spoken Conversational Search System (SCSS) which provides a conversational approach to defining user information needs, presenting results and enabling search reformulations. In order to contribute to a more efficient and effective search experience when using a SCSS, we intend for a tighter integration between document search and conversational processes

You Can Check It Out But It Will Never Leave: Characterising Ebook Borrowing Patterns

What does it mean for a reader to borrow an ebook? Ebook technology means that borrowing can take different forms, for example printing and reading. We do not know, though, which of these options readers actually use. Ebook technology generates logs that allow us to understand ebook borrowing patterns over time, both by individual readers and in aggregate. Despite the ready availability of ebook logs, this area remains under-researched. In this paper we present an exploratory log analysis of ebook borrowing, comparing printing and reading, discovery patterns, single- and multiple-book sessions and identifying specific borrowing patterns

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes. Copyright © 2022 von Hoyer, Hoppe, Kammerer, Otto, Pardi, Rokicki, Yu, Dietze, Ewerth and Holtz