Bilingual children in early elementary grades (K-2) often encounter a significant math-literacy barrier, where an English language gap, rather than a lack of mathematical understanding, hinders their academic progress. Math homework seems to be particularly challenging for bilingual children when their parents are also not fluent in English. This paper presents an exploratory study showing how a bilingual homework solution may help close the language gap. The study evaluated MathTarea, a bilingual homework application that enables students and their parents to see homework problems in two languages. Initial results suggest that such an application has the potential to positively impact at-home practice, indicating that reducing the language barrier is a critical step toward more equitable education in bilingual communities.

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, including their language proficiencies. In particular, there is now a significant number of polyglot Web users, i.e., users who are proficient in more than one language. However, even such users with potential access to a broad range of information from multiple languages often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages. To overcome language barriers, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. In particular, relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences, and even less so regarding news search and different access modalities (such as desktop and mobile interfaces). The research presented in this article provides the first comparative analyses of polyglot users’ preferences and behaviors with respect to different multilingual news search interfaces on both desktop and mobile platforms. Specifically, through a set of task-based user studies in laboratory experiments, the key contribution of this article is the presentation of the first human-centered studies in multilingual news search result interfaces, aiming to drive the development of human-centered multilingual news access systems for both desktop and mobile platforms. This contribution includes a detailed analysis of different interface design paradigms, as well as a series of implications for design.

Designing and developing innovative visualizations to assist humans in the process of generating and understanding complex semantic data has become an important element in supporting effective human-ontology interaction, as visual cues are likely to provide clarity, promote insight, and amplify cognition. While recent research has indicated potential benefits of applying novel adaptive technologies, typical ontology visualization techniques have traditionally followed a one-size-fits-all approach that often ignores an individual user's preferences, abilities, and visual needs. In an effort to realize adaptive ontology visualization, this paper presents a potential solution to predict a user's likely success and failure in real time, and prior to task completion, by applying established machine learning models on eye gaze generated during an interactive session. These predictions are envisioned to inform future adaptive ontology visualizations that could potentially adjust its visual cues or recommend alternative visualizations in real time to improve individual user success. This paper presents findings from a series of experiments to demonstrate the feasibility of gaze-based success and failure predictions in real time that can be achieved with a number of off-the-shelf classifiers without the need of expert configurations in the presence of mixed user backgrounds and task domains across two commonly used fundamental ontology visualization techniques.

User language proficiency has been shown to significantly affect search result language preferences, search behaviors, and even interface preferences. Consequently, search systems may consider adapting to a user's language proficiency when retrieving, composing, and presenting search results. In order to perform such adaptation, it is necessary to first get an estimate of a user's proficiency, ideally through simply observing their behaviors while searching. To this end, this paper investigates the extent to which a user's language proficiency can be inferred from their eye movements while they are evaluating search results. Classification results involving data from English-, Spanish-, and Chinese-speaking study participants show that such an inference is indeed possible, and with relatively high accuracies for all languages. It is also shown that feature sets involving statistics from an entire search result page, combined with gaze data from individual results, have the highest classification accuracies. Moreover, a user's Average Fixation Durations, Refixations, and Pupil Dilations are found to be the most significant features.

Abstract Many users of search systems are multilingual, that is, they are proficient in two or more languages. In order to better understand and support the language preferences and behaviors of such multilingual users, this paper presents a series of five large‐scale studies that specifically elicit language choices regarding search queries and result lists. Overall, the results from the studies indicate that users frequently make use of different languages (i.e., not just their primary language), especially when they are provided with choices (e.g., when provided with a secondary language query or result list choice). In particular, when presented with a mixed‐language list choice, participants choose this option to an almost equal extent compared to primary‐language‐only lists. Important factors leading to language choices are user‐, task‐ and system‐related, including proficiency, task topic, and result layout. Moreover, participants' subjective reasons for making particular choices indicate that their primary language is considered more comfortable, that the secondary language often has more relevant and trustworthy results, and that mixed‐language lists provide a better overview. These results provide crucial insights into multilingual user preferences and behaviors, and may help in the design of systems that can better support the querying and result exploration of multilingual users.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. HAAPIE workshop embraces the essence of the "human-machine co-existence" and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the fifth edition of HAAPIE includes 5 long papers.

With estimates suggesting that half of the world's population learns or speaks at least two languages, Web information access systems such as Web search engines need to cater for an increasing variety of individual language proficiencies and preferences. However, while significant advances have been made regarding the handling, retrieval, and automatic translation of multilingual information, there has been a relative lack of user-centered research aiming to support individual users' multilingual abilities. To address this research gap, this paper presents a series of user studies and experiments that aim to inform novel search solutions that specifically support multilingual users. In particular, the experiments presented in this paper examine the extent to which a system can predict, for a given query, what language(s) a multilingual user would prefer the search results to be in. Results from our studies show that such predictions can statistically significantly outperform a baseline model, and that users' languages and proficiencies, their current location, as well as the search topic domain and type all influence the prediction results.

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.

Chenjun Linga* , Ben Steichenb & Silvia Figueiraaa Department of Computer Engineering, Santa Clara University, Santa Clara, CA, USAb Department of Computer Science, California State Polytechnic University, Pomona, CA, USAChenjun Ling Ph.D. candidate in the Computer Science and Engineering at Santa Clara University. Her research interests include Information Retrieval, HCI, and Data Science.Ben Steichen is an Assistant Professor in the Computer Science Department at California State Polytechnic University, Pomona. Dr. Steichen’s research focuses on Human-Centered Computing and Personalized Information Access, which involves techniques and concepts from the fields of HCI, Information Retrieval & Visualization, and Web & Data Science.Silvia Figueira is a Professor of Computer Science and Engineering at Santa Clara University and the director of the SCU Frugal Innovation Hub. Her research is in the area of performance evaluation and prediction, recently with a focus on energy efficiency.CONTACT Chenjun Ling [email protected]; [email protected] Department of Computer Engineering, Santa Clara University, 500 El Camino Real, 95053, Santa Clara, CA, USA.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/hihc.ABSTRACTWith the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, particularly in terms of languages. In fact, there are now many users online who are polyglots, i.e. users who are proficient in more than one language. However, even such multilingual users often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages, even though prior research results have shown that multilingual users make significant use of each of their languages when generally searching for information online.The research presented in this paper provides the first investigation of multilingual news consumption, as well as a detailed analysis of multilingual querying and search result selection behaviors. In particular, through a set of 2 phases of crowdsourced user studies, this paper presents the first human-centered studies in multilingual news access, aiming to drive the development of personalized multilingual news access systems that better support each individual user.

While the number of polyglot Web users across the globe has increased dramatically, little human-centered research has been conducted to better understand and support multilingual user abilities and preferences. In particular, in the fields of cross-language and multilingual search, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. By contrast, this paper specifically focuses on multilingual search user interface preferences and behaviors, through a lab-based user study involving 25 participants interacting with a set of four different interactive multilingual search user interfaces. User preference results confirm that multilingual search users generally have strong preferences towards interfaces that provide clear language separation, and that the traditional approach of interleaving results, as typically used in prior research, is least preferred. In addition, an analysis of user interaction behaviors shows that multilingual users make significant use of each of their languages, and that there are several interaction behavior differences depending on interface and task type.

The unprecedented increase in online search user diversity across the globe has led to new challenges for search engine providers. In particular, among these challenges is the need to better support individuals who are proficient in multiple languages. To investigate this particular user characteristic, this paper presents an analysis of multilingual search user behaviors through a series of large-scale studies using crowdsourcing. Results show that multilingual users make significant use of each of their languages when searching, and that there are significant differences in behaviors between querying and result selection. In addition, results show that language use strongly depends on a number of task factors and individual user characteristics. These results are discussed in terms of building novel adaptive multilingual search solutions that better support and adapt to users who have multiple language abilities.

The unrelenting rise in online user diversification has generated tremendous new challenges for search system providers. Among these, the need to address multiple user language abilities and preferences is paramount. The majority of research on multilingual search has so far focused on improving retrieval and translation techniques in cross-language information retrieval. However, less research has focused on the human-computer interaction aspects of multilingual search, particularly in terms of multilingual result display interfaces. To address this research gap, this paper presents a comparison of 5 different search interface designs for multilingual search. We analyze and evaluate these interfaces through a crowd-based experiment involving 885 participants. Our results show that the common approach of interleaving multilingual results is in fact the least preferred, whereas single-page displays with clear language separation are most preferred. In addition, we show that user proficiency and search content type play an important role in user preferences, and that different interfaces elicit different user behaviors.

There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.

A key challenge for information visualization designers lies in developing systems that best support users in terms of their individual abilities, needs, and preferences. However, most visualizations require users to first gather a certain set of skills before they can efficiently process the displayed information. This paper presents a first step towards designing visualizations that provide personalized support in order to ease the so-called 'learning curve' during a user's skill acquisition phase. We present prediction models, trained on users' gaze data, that can identify if users are still in the skill acquisition phase or if they have gained the necessary abilities. The paper first reveals that users exhibit the learning curve even during the usage of simple information visualizations, and then shows that we can generate reasonably accurate predictions about a user's skill acquisition using solely their eye gaze behavior.

Abstract There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.

Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.

There is increasing evidence that users' characteristics such as cognitive abilities and personality have an impact on the effectiveness of information visualization techniques. This paper investigates the relationship between such characteristics and fine-grained user attention patterns. In particular, we present results from an eye tracking user study involving bar graphs and radar graphs, showing that a user's cognitive abilities such as perceptual speed and verbal working memory have a significant impact on gaze behavior, both in general and in relation to task difficulty and visualization type. These results are discussed in view of our long-term goal of designing information visualisation systems that can dynamically adapt to individual user characteristics.

Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.