Relating eye-tracking measures with changes in knowledge on search tasks

Bhattacharya, Nilavra; Gwizdka, Jacek

doi:10.1145/3204493.3204579

Cited by 25 publications

(19 citation statements)

References 25 publications

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“…In contrast to the aforementioned studies that considered just a handful of predictors, Yu et al [51] evaluated approximately 70 search-based features as predictors of learning; individually they were found to be only weakly correlated with knowledge gain, though some (document dwell time, query complexity) were more predictive than others. Although unarguably less scalable, a number of eye-tracking measures (such as the duration or reading fixations within documents) have also shown to be predictive of learning outcomes [3,4]. Finally, we note that von Hoyer et al [46] also found learners to be able to estimate their learning performance with increasing accuracy as the search session progresses.…”

Section: Related Workmentioning

confidence: 75%

“…Recently, a number of different research efforts have been devoted to the area of SAL, such as: (i) the influence of user characteristics and user strategies on learning while searching [13,20,22,29,30,34]; (ii) the exploration of user behavior during learning-oriented search sessions [12,13,27,34]; (iii) the prediction/observation of how knowledge changes over time and across different cognitive levels of learning [18,21,34,49,51]; (iv) the measuring of learning during searches [3,4,12,46,50]; and (v) the design of retrieval algorithms for learning-oriented search tasks and user interface components [40,42,43]. Despite the large number of prior works in the SAL field, only a small number have so far explored the adaptation of the search system itself to improve learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Searching to Learn with Instructional Scaffolding

Câmara

Roy

Maxwell

et al. 2021

Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

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Web search engines are today considered to be the primary tool to assist and empower learners in finding information relevant to their learning goals-be it learning something new, improving their existing skills, or just fulfilling a curiosity. While several approaches for improving search engines for the learning scenario have been proposed (e.g. a specific ranking function), instructional scaffolding (or simply scaffolding)-a traditional learning support strategy-has not been studied in the context of search as learning, despite being shown to be effective for improving learning in both digital and traditional learning contexts. When scaffolding is employed, instructors provide learners with support throughout their autonomous learning process. We hypothesize that the usage of scaffolding techniques within a search system can be an effective way to help learners achieve their learning objectives whilst searching. As such, this paper investigates the incorporation of scaffolding into a search system employing three different strategies (as well as a control condition): (i) AQE SC , the automatic expansion of user queries with relevant subtopics; (ii) CURATED SC , the presenting of a manually curated static list of relevant subtopics on the search engine result page; and (iii) FEEDBACK SC , which projects real-time feedback about a user's exploration of the topic space on top of the CURATED SC visualization. To investigate the effectiveness of these approaches with respect to human learning, we conduct a user study (𝑁 = 126) where participants were tasked with searching and learning about topics such as 'genetically modified organisms'. We find that (i) the introduction of the proposed scaffolding methods in the proposed topics does not significantly improve learning gains. However, (ii) it does significantly impact search behavior. Furthermore, (iii) immediate feedback of the participants' learning (FEEDBACK SC ) leads to undesirable user behavior, with participants seemingly focusing on the feedback gauges instead of learning.

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Section: Related Workmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Searching to Learn with Instructional Scaffolding

Câmara

Roy

Maxwell

et al. 2021

Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

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“…Bhattacharya and Gwizdka (2018) [79] Users with higher change in knowledge differed significantly in terms of their total reading-sequence length, reading-sequence duration, and number of reading fixations, when compared to participants with lower knowledge change.…”

Section: Papoutsaki Et Al (2017) [8]mentioning

confidence: 98%

Eye-Tracking Studies of Web Search Engines: A Systematic Literature Review

Strzelecki

2020

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This paper analyzes peer-reviewed empirical eye-tracking studies of behavior in web search engines. A framework is created to examine the effectiveness of eye-tracking by drawing on the results of, and discussions concerning previous experiments. Based on a review of 56 papers on eye-tracking for search engines from 2004 to 2019, a 12-element matrix for coding procedure is proposed. Content analysis shows that this matrix contains 12 common parts: search engine; apparatus; participants; interface; results; measures; scenario; tasks; language; presentation, research questions; and findings. The literature review covers results, the contexts of web searches, a description of participants in eye-tracking studies, and the types of studies performed on the search engines. The paper examines the state of current research on the topic and points out gaps in the existing literature. The review indicates that behavior on search engines has changed over the years. Search engines’ interfaces have been improved by adding many new functions and users have moved from desktop searches to mobile searches. The findings of this review provide avenues for further studies as well as for the design of search engines.

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“…Search as Learning. Previous research within the SAL domain has focused on: (i) understanding user behaviours when undertaking a learning-oriented search task [9,17,24,29,32,36]; (ii) exploring different types of users and their behaviours (e.g., novices vs. experts) [6,17,39,44]; and (iii) the optimisation of retrieval functions for learning [47][48][49].…”

Section: Related Workmentioning

confidence: 99%

Note the Highlight

Roy

Torre

Gadiraju

et al. 2021

Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

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Active reading strategies-such as content annotations (through the use of highlighting and note-taking, for example)-have been shown to yield improvements to a learner's knowledge and understanding of the topic being explored. This has been especially notable in long and complex learning endeavours. With web search This research has been supported by DDS (Delft Data Science) and NWO projects SearchX (639.022.722) and Aspasia (015.013.027).

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Relating eye-tracking measures with changes in knowledge on search tasks

Cited by 25 publications

References 25 publications

Searching to Learn with Instructional Scaffolding

Searching to Learn with Instructional Scaffolding

Eye-Tracking Studies of Web Search Engines: A Systematic Literature Review

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