Engage!: Co-designing Search Engine Result Pages to Foster Interactions

Allen, Garrett; Peterson, Benjamin L; Ratakonda, Dhanush kumar; Sakib, Mostofa Najmus; Fails, Jerry Alan; Kennington, Casey; Wright, Katherine Landau; Pera, Maria Soledad

doi:10.1145/3459990.3465183

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…As such, we constructed a dataset 2 using data from various sources, which we summarize in Table 1. This dataset includes: (i) adult queries from the TREC data [26], (ii) queries first introduced in the work conducted by Madrazo Azpiazu et al [23] (henceforth referred to as Sven), each indicating whether it was formulated by a child (ages 8 to 11) or adult, and (iii) queries formulated by children ages 7 to 11 years old, which we refer to as CAST data 3 , that resulted from several user studies conducted to examine children's search behavior [1,2,4,8,10].…”

Section: Search Query Datamentioning

confidence: 99%

“Who are you?”: Identifying Young Users from a Single Search Query

Bettencourt,

Manzi Muneza,

Green

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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As an initial step towards enabling the adaptation of (popular, and widely used) web search environments so that they can better serve children and ease their path towards information discovery, we introduce Recognizing Young Searchers (RYSe). RYSe leverages lexical, syntactical, spelling/punctuation, and vocabulary features that align with the Concrete Operational stage of development (originally identified by Jean Piaget) in an attempt to identify users that are in this stage. The concrete operational stage is commonly associated with children ages 7-11. Findings emerging from our initial empirical exploration using single queries formulated by children and sample queries from adults showcase the feasibility of relying on different cognitive traits inferred from the short text of a single query to distinguish those that are formulated by younger searchers.

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Section: Search Query Datamentioning

confidence: 99%

“Who are you?”: Identifying Young Users from a Single Search Query

Bettencourt,

Manzi Muneza,

Green

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

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“…When looking at design considerations for conversational search devices for children, processes like participatory design [37], Wizard of Oz [40] or cooperative inquiry techniques [13,16] that include children as design partners have been considered. The latter has been employed in explorations into what a SERP should look like and preferences for traits and personification of voice assistants, according to children [1,25,43]. A similar process can be undertaken to seek balance between what children want in a conversational search interface and what they need.…”

Section: Children Conversation and The Classroom: What's Next?mentioning

confidence: 99%

Using Conversational Artificial Intelligence to Support Children's Search in the Classroom

Allen¹,

Yang²,

Pera³

et al. 2021

Preprint

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“…Another factor that should be taken into account when it comes to engaging with a search engine is the user themselves. For instance, Alen et al [31] argued that children (between 6 and 12 years old) desired more dynamic means of navigating search results and icon-based interaction options. According to White, people favored positive results over negative ones and sought confirmation of their beliefs, when searching online [32], implying a bias or an echo chamber effect.…”

Section: Introductionmentioning

confidence: 99%

Searching Online for Art and Culture: User Behavior Analysis

et al. 2023

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With the constant expansion of the Web, search engines became part of people’s daily routines. How users behave during the search process depends on a variety factors, one of which is the topic of their search interest. This study focused on the behavior of users searching the Web for content related to art and cultural heritage. A proprietary, publicly available, federated search engine, in the form of a web and mobile app, was developed for this research’s purposes. This platform was used to monitor actual user behavior during a six-month period. Quantitative data related to the platform’s usage were collected and analyzed in order to provide a detailed picture of the way interested parties engaged with it. This information pertained not only to the search queries and results viewed, but also to the various characteristics of the search sessions themselves. The study presented an analysis of these data, with emphasis on query and result characteristics, usage devices, login preferences and session duration, and drew conclusions. The study’s findings showed, among other things, that art searchers showed a preference for shorter queries, a tendency for higher query repeatability, and showed interest in a wider number of results than general purpose searchers. Additionally, they were more keen to use desktop devices instead of mobile ones and displayed higher engagement metrics during longer search sessions or when logged in. These findings outlined an art searcher who was interested in concepts and people often revisited searches and results, showed interest for more than the first few hits, was attracted by rich content, and understood the art search process as a task which requires focus. They also pointed out a duality in the art search process itself which can be long and involved or short and purposeful.

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Engage!: Co-designing Search Engine Result Pages to Foster Interactions

Cited by 8 publications

References 11 publications

“Who are you?”: Identifying Young Users from a Single Search Query

“Who are you?”: Identifying Young Users from a Single Search Query

Using Conversational Artificial Intelligence to Support Children's Search in the Classroom

Searching Online for Art and Culture: User Behavior Analysis

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