Abstract As the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: “Stable high rating pattern,” “Variable medium rating pattern,” and “Unstable low rating pattern.” Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human‐centered algorithmic practices and HCI design.

Abstract The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data‐driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human‐centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

Good abandonment behavior means that in a query, the user can obtain the required information directly through the search results without clicking any linked page or reformulating the search query, which is common in mobile search at present. In this paper, a good abandonment prediction model in mobile search was constructed from 5 groups of features: session features, query features, SERP features, mobile touch interaction features and visual attention features. The visual attention features are introduced into good abandonment prediction for the first time and proved to be able to improve the prediction accuracy.

Abstract It is now common for individuals to have multiple computing devices, such as laptops, smart phones, and tablets. This multidevice environment increases the popularity of cross‐device search activities. Cross‐device search can be seen as a special case of cross‐session search. Previous studies regarded re‐finding behaviors in cross‐session search as task resumption. Based on this, this article proposes considering 2 phases of cross‐device search: task preparation and task resumption and to explore their features by modeling. A within‐subject user experiment was designed to collect data. Four groups of features were captured from specific behaviors of querying, clicking, gazing, and cognition. This article tested 3 machine‐learning methods and found that the C5.0 decision tree performed best. Five features were included in the task preparation behavior model, and 3 in the task resumption behavior model. The difference and relationship between task preparation and task resumption were investigated by comparing their behavioral features. It is concluded that information need remains blurred in task preparation and becomes clear in task resumption. The changing states of information need suggest an exploratory process in cross‐device search. We also identify some implications for search engine designers.

Abstract In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID‐19) epidemic, we (a) examine challenges associated with what we term “global information crises”; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short‐ and long‐term research, education, and practice in information science.

Enlightened by task resumption behaviors in cross-session search, we explore information resumption behaviors in cross-device search. In order to find important features of information resumption behaviors, we conducted a user experiment and modeled information resumption behaviors using machine learning. The model of C5.0 Decision Tree outperformed and showed that features of FamiliarityScores, AveEditDistance, AveQueryEffectiveRate and ValidClickRate are of importance.

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval (CLIR) settings. The studies of RF in CLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in CLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolve some out-of-vocabulary terms. In this paper, we propose a novel RF method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra translation alternatives if available so that the translated queries are more tuned to the current search. We studied TE using pseudo relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve CLIR with both types of RF methods, and that the improvement is comparable to that of QE. More importantly, the effects of TE and QE are complementary. Their integration can produce further improvement, and makes CLIR more robust for a variety of queries.

No abstract available.