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.

Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the link between readability and IR is often overlooked. As a step towards advancing knowledge on the influence of readability on IR, we focus on Web search for children. We explore how traditional formulas–which are simple, efficient, and portable–fare when applied to estimating the readability of Web resources for children written in English. We then present a formula well-suited for readability estimation of child-friendly Web resources. Lastly, we empirically show that readability can sway children’s information access. Outcomes from this work reveal that: (i) for Web resources targeting children, a simple formula suffices as long as it considers contemporary terminology and audience requirements, and (ii) instead of turning to Flesch-Kincaid–a popular formula–the use of the “right” formula can shape Web search tools to best serve children. The work we present herein builds on three pillars: Audience, Application, and Expertise. It serves as a blueprint to place readability estimation methods that best apply to and inform IR applications serving varied audiences.

Children's ability to spell effectively is a major barrier to using search engines successfully. While search engines make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users (i.e., adults) and have proven inadequate for children. The specific target of children for this research are those with early literacy skills (whose are typically ages 6–12). The aim of this work is twofold: first, to address the types of spelling errors children make by researching, developing, and evaluating algorithms to generate and rank candidate English spelling suggestions for children; and second, to improve children's user experience when using our proposed spellchecker by involving them in the design process through participatory design and evaluating the impact of interactive elements on children's spellchecking behaviors. The outcomes of our studies and assessments result in a phonetic-based spelling correction model (KidSpell) that can more accurately correction children's spelling errors than existing state-of-the-art models. Further, we learned that visual and audio cues have a positive impact on children's ability to find their intended word from a list of spelling suggestions.

The role that technology plays in supporting children at school and at home is more prominent than ever before due to the global COVID-19 pandemic. This has prompted us to focus the 6th International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) workshop on what the lasting changes will be to the design and development of child information retrieval systems. After two years, are information retrieval systems used more in and out of the classroom? Are they more interactive, more or less personalized? What is the impact on the research and business community? Are there long-term and unexpected changes on the design, ethics, and algorithms? The primary goal of our workshop continues to be to build community by bringing together researchers, practitioners, and other stakeholders from various backgrounds and disciplines to understand and advance information retrieval systems for children.

In this paper, we present BiGBERT, a deep learning model that simultaneously examines URLs and snippets from web resources to determine their alignment with children’s educational standards. Preliminary results inferred from ablation studies and comparison with baselines and state-of-the-art counterparts, reveal that leveraging domain knowledge to learn domain-aligned contextual nuances from limited input data leads to improved identification of educational web resources.

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children's engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children's preferences and determine what children (ages 6-12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements-CHIRP-can foster among children, we conducted a user study at a public school. Analysis of children's interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP.

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a systematic literature review of work that illustrate challenges and limitations, we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.

In this past year, the role of technology to support education has been more prominent than ever. This has prompted us to focus the 5th Edition of the International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) around a major stakeholder when it comes to technology adoption for the classroom: the teacher. Much like in the previous editions of the workshop, our priority remains understanding what is good when it comes to information retrieval systems for children, this time from the perspectives of teachers. In order to control scope of our discussion and continue to advance knowledge in this area, we focus on technology that enables information discovery in the classroom context: search and recommendation systems. Expected outcomes from this workshop remain building community in this important area of work and extend the framework we have been defining to enable design and evaluation of good information retrieval systems for children.

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of KidSpell, we compare the model's performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.

Spellchecking functionality embedded in existing search tools can assist children by offering a list of spelling alternatives when a spelling error is detected. Unfortunately, children tend to generally select the first alternative when presented with a list of options, as opposed to the one that matches their intent. In this paper, we describe a study we conducted with 191 children ages 6-12 in order to offer empirical evidence of: (1) their selection habits when identifying spelling suggestions that match the word they meant to type, and (2) the degree of influence multimodal cues, i.e., synthesized speech and images, have in prompting children to select the correct spelling suggestion. The results from our study reveal that multimodal cues, primarily synthesized speech, have a positive impact on the children's ability to identify their intended word from a list of spelling suggestions.

Data about people are constantly collected and stored to help understand patterns which can then be leveraged by information retrieval systems (IRS), such as search engines or recommender systems, to identify and rank resources that respond to diverse users' needs. As a significant group of technology users, children's data are also collected for IRS. In the 4th edition of our proposed workshop, we seek to continue to build professional community connected to children's IRS and expand on the framework identified in the 3rd KidRec that outlines how to evaluate good IRS. This time, we are particularly interested in exploring how design, research, and practice perspectives can cohesively define policy in this area.

Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward-we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation-including query suggestion algorithms-for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation, (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind, and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations, the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.

Today's children spend considerable time online, searching and receiving information from various websites and apps. While searching for information, e.g. for school or hobbies, children use search systems to locate resources and receive site recommendations that might be useful for them. The call for good, reliable, child-friendly systems has been made many times and the thesis that the algorithms of "adult" information systems are not necessarily suitable or fair for children is widely accepted. However, there is still no clear and balanced view on what makes one search/recommendation system for children good or better than other systems, nor on what content should be considered "good enough to be retrieved" or recommended. The goal of this workshop is to bring together researchers and practitioners in education, child-development, computer science, and more who can address this questions while considering issues related to education, algorithms, ethics, privacy, evaluation.

Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers.

Popular tools used to search for online resources are tuned to satisfy a broad category of users-primarily adults. Because children have specific needs, these tools may not always be successful in offering the right level of support in their quest for information. While search tools often provide query assistance, children still face many difficulties expressing their information needs in the form of a query. In this paper, we share results from our ongoing research work focused on understanding children's interactions with query suggestions and their preferences with respect to suggestions offered by a general-purpose strategy versus a counterpart designed exclusively for children. Our goal is to inform researchers and developers about when it is necessary to turn to technologies tailored exclusively for children and to further outline needs that should be addressed when it comes to designing query-formulation-related technology for children.

Information discovery tasks using online search tools are performed on a regular basis by school-age children. However, these tools are not necessarily designed to both explicitly facilitate the retrieval of resources these young users can comprehend and aid low-literacy searchers. This is of particular concern for educational environments, as there is an inherent expectation that these tools facilitate effective learning. In this manuscript we present an initial assessment conducted over (1) children-oriented search tools based on queries generated by K-9 students, analyzing features such as readability and adequacy of retrieved results, and (2) tools used by teachers in their classrooms, analyzing their main purpose and target audience’s age range. Among the examined tools, we include YouUnderstood.Me, an enhanced search environment, which is the result of our ongoing efforts on the development of a search environment tailored to 5-15 year-olds that can foster learning through the retrieval of materials that not only satisfy the information needs of these users but also match their reading abilities. The results of these studies highlight the fact that search results presented to children have average reading levels that do not match the target audience. In addition, tools oriented to teachers do not go beyond showing the progress of their students, and seldomly provide a simple way of retrieving class contents that fit current needs of students. These facts further showcase the need for developing a dual environment oriented to both teachers and students.

The 1^st Workshop on Children and Recommender Systems (KidRec) is taking place in Como, Italy August 27^th, 2017 in conjunction with the ACM RecSys 2017 conference. The goals of the workshop are threefold: (1) discuss and identify issues related to recommender systems used by children including specific challenges and limitations, (2) discuss possible solutions to the identified challenges and plan for future research, and (3) build a community to directly work on these important issues.