Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web

Gadiraju, Ujwal; Yu, Ran; Dietze, Stefan; Holtz, Peter

doi:10.1145/3176349.3176381

Cited by 76 publications

(88 citation statements)

References 20 publications

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“…In addition, the tasks were designed such that searchers had to search for multiple aspects describing the information need [9]. To observe the difference between typical IR tasks and SST in terms of the effect of user mood, we picked another 10 typical IR tasks from previous work by Gadiraju et al that was based on the TREC 2014 Web Track dataset 4 [7]. Table 1 presents samples of the selected typical IR tasks and the generated struggling text retrieval tasks.…”

Section: Tasksmentioning

confidence: 99%

Revealing the Role of User Moods in Struggling Search Tasks

Zhou

Gadiraju

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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User-centered approaches have been extensively studied and used in the area of struggling search. Related research has targeted key aspects of users such as user satisfaction or frustration, and search success or failure, using a variety of experimental methods including laboratory user studies, in-situ explicit feedback from searchers and by using crowdsourcing. Such studies are valuable in advancing the understanding of search difficulty from a user's perspective, and yield insights that can directly improve search systems and their evaluation. However, little is known about how user moods influence their interactions with a search system or their perception of struggling. In this work, we show that a user's own mood can systematically bias the user's perception, and experience while interacting with a search system and trying to satisfy an information need. People who are in activated-(un)pleasant moods tend to issue more queries than people in deactivated or neutral moods. Those in an unpleasant mood perceive a higher level of difficulty. Our insights extend the current understanding of struggling search tasks and have important implications on the design and evaluation of search systems supporting such tasks.

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Section: Tasksmentioning

confidence: 99%

Revealing the Role of User Moods in Struggling Search Tasks

Zhou

Gadiraju

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Research in search as learning has proposed several ways to measure the learning outcome after searching (Bhattacharya & Gwizdka, ; Collins‐Thompson, Rieh, Haynes, & Syed, ; Gadiraju, Yu, Dietze, & Holtz, ; Wilson & Wilson, ; Yu et al, ); and the relationship between search interactions and learning performance or knowledge change (Bhattacharya & Gwizdka, ; Ghosh, Rath, & Shah, ; Liu & Song, ).…”

Section: Introductionmentioning

confidence: 99%

Investigation of users' knowledge change process in learning‐related search tasks

Liu

Belkin

2019

Proceedings of the Association for Information Science and Tech

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This study aims to examine users' knowledge change characteristics and knowledge change process, and examined factors that may influence users' knowledge change process. A user search experiment was conducted, in which participants were asked to search for two learning‐related search tasks and use a mind‐map to organize their thoughts during the search process. Pre‐ and post‐search questionnaires were distributed to collect users' self‐assessed topic familiarity and task difficulty. Besides the overall knowledge change process by all the users, we identified four types of knowledge change styles using a hierarchical clustering method: Early knowledge change, Medium‐term knowledge change, Late knowledge change and Average knowledge change. In addition, we found that task type, task difficulty in information integration and difficulty in drawing the mind‐map had significant influences on users' knowledge change styles. This study took a process perspective to characterize users' knowledge change at different stages and shed light on when and how learning occurs during the search process.

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“…The video transcript does also contain a lot of useful information regarding speech quality. Since the influence of textual content on the knowledge gain has been examined extensively (e.g., by Gadiraju et al [12]), this information is disregarded here and the focus set on the lesser researched features. However, we make use of the content of the transcript in a different way in section 2.2.…”

Section: Feature Description Loudnessmentioning

confidence: 99%

Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality

Shi

Otto

Hoppe

et al. 2019

Proceedings of the 1st International Workshop on Search as Learning With Multimedia Information

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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.

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Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web

Cited by 76 publications

References 20 publications

Revealing the Role of User Moods in Struggling Search Tasks

Revealing the Role of User Moods in Struggling Search Tasks

Investigation of users' knowledge change process in learning‐related search tasks

Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality

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