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With the progress of the times and the improvement of science and technique, network message technique has occupied a vital position in people's lives. At the same time, society has been implementing university English education reform in recent years, and the "internet plus" wisdom education model is the product of the improvement of the times. This new education model has gradually integrated into the education of various subjects. Introducing the concept of message technique and wisdom education into university translation education can innovate education mode, optimize education content, and integrate high-quality education resources. Cultivating applied translators has become the trend of educational reform. Based on deep learning, this paper studies translation education skills in universities. In-depth education enables learners to acquire systematic knowledge, critical spirit, creative thinking, etc. This kind of learning fully taps individual potential to cultivate a complete personality. According to the research in this paper, wisdom education is 12% better than traditional education, and it is suitable to be widely put into practice. • This new education model has gradually integrated into the education of various subjects. • Enhancing university translation education with message technique and wisdom education modernize educational approach. • Cultivating applied translators has become the trend of educational reform. • Based on deep learning, this paper studies university translation education skills. [ABSTRACT FROM AUTHOR]

In recent years, university students have reported increased symptoms of stress and anxiety, which can negatively impact their mental well-being and academic performance. However, many students do not seek or receive support for these challenges. With recent technological advances in intelligent agents and their expanding capabilities, there is a potential for intelligent agents to extend and complement mental health care interventions and enhance access to care. Recognizing that there are many further steps to developing user-centred evidence-based approaches for mental health challenges, as a first step to developing effective interventions using intelligent agents, it is important to identify design elements and functionalities that are perceived as engaging and useful by students. In this paper, we present the findings of an online survey with 1054 undergraduate students to explore students' perceptions of, and preferences for, using different types of intelligent agents (e.g., virtual agents, social robots, etc.) to support their mental well-being, specifically to cope with feelings of stress and anxiety in social situations typically encountered within a university context (e.g., engaging in a group discussion, delivering presentations, expressing opinions, etc.). Students were asked to complete a questionnaire to explore their experience of stress and anxiety in the university context, as well as their perceptions of, and preferences for using different intelligent agents as assistive tools to cope with such feelings. The results provide insights into different design elements as well as social and technical capabilities to consider when designing intelligent agents to help address stress and anxiety among university students. • We surveyed 1054 undergraduate students on using intelligent agents to manage anxiety. • Public speaking was reported as the most anxiety-provoking situation at university. • Students preferred animal-like robots and conversational agents to cope with anxiety. • Intelligent agents would help manage anxiety by providing personalized coaching. • We discuss design implications for intelligent agents supporting students' anxiety. [ABSTRACT FROM AUTHOR]

By constructing an integrated model that delves into the intricate relationship between algorithmic ethical perceptions (i.e., fairness, accountability, and transparency), algorithmic legitimacy, and continuous usage intentions of e-commerce platforms, this study aims to investigate the antecedents and consequences of algorithmic legitimacy. Through the utilization of structural equation modeling, we analyzed 488 questionnaires, revealing that the fairness, accountability, and transparency of algorithmic processes within e-commerce platforms positively affected algorithmic legitimacy. Additionally, algorithmic legitimacy exerted a positive influence on the continuous usage intention of e-commerce platforms. Algorithmic legitimacy mediated the effect of algorithmic fairness, accountability, and transparency on the continuous usage intention of e-commerce platforms. Furthermore, this study unearthed the moderating role of user innovativeness and found it weakened the positive impact of algorithmic legitimacy on the continuous usage intention of e-commerce platforms. This study provides valuable insights into legitimacy theory in the domain of algorithms by introducing the conceptualization of algorithmic legitimacy and examining its antecedents and consequences. Moreover, this study enhances our current understanding of ethical considerations related to algorithms in the context of digital intelligence. • Algorithmic fairness, accountability, and transparency positively affect algorithmic legitimacy. • Algorithmic legitimacy positively affects continuous usage intention of e-commerce platforms. • User innovativeness weakens the positive effect of algorithm legitimacy on continuous usage intention of platforms. [ABSTRACT FROM AUTHOR]

Teens inhabit an algorithmically curated digital world. Thus, algorithm awareness has emerged as a critical digital skill for parents of teens to mitigate online risks and maximize online opportunities. This study explores how parents' algorithm awareness of the TikTok "For You" feed relates to perceived online risks and opportunities as well as parental mediation strategies. A survey of U.S. parents found that greater algorithm awareness on TikTok was associated with more negative attitudes about the TikTok algorithm via the mediator of perceived TikTok risks. Parents with negative attitudes about algorithms on TikTok were more likely to prohibit their teenager from using TikTok, but algorithm awareness and attitudes were unassociated with other parental mediation strategies. This study contributes to research on digital parenting practices, suggesting that (a) perceptions of risks, not opportunities, frame the algorithm awareness process for parents and (b) algorithm awareness may operate differently than other digital skills. • For parents of teens, algorithm awareness predicted more perceived risks but not opportunities from TikTok use. • A negative relationship between algorithm awareness and attitudes was explained by perceived TikTok risks. • Negative attitudes about TikTok's algorithm predicted greater likelihood of banning teens from using TikTok. • Algorithm awareness and attitudes were unrelated to enabling parental mediation. [ABSTRACT FROM AUTHOR]

This study examines whether providing users with declarative and procedural privacy knowledge positively affects behaviors to safeguard their privacy. Two forms of coping behaviors were in focus: Measures that reduce data collection by technological means (privacy-protective behaviors) and self-restraint in the use of online services (chilling effect-related behaviors). We hypothesized that a combination of declarative knowledge and procedural knowledge as opposed to declarative knowledge alone increases privacy-protective behaviors while being less likely to induce chilling effect-related behaviors. Data were obtained using an experimental study design combined with browser histories. Individuals were exposed to video treatments providing them with a) declarative knowledge or b) a combination of declarative and procedural knowledge, while the control group received no treatment. The study attracted a total of N = 500 participants for self-report data and N = 252 participants with data donations from a student sample in Austria. The findings indicate that both knowledge treatments increased recipients' privacy concerns. Furthermore, the declarative and procedural knowledge treatment increased participants' self-efficacy. However, increasing knowledge did not translate into more pronounced privacy behaviors. Tentative evidence suggests that privacy fatigue might play a particularly important role in explaining these null findings. • Procedural and declarative privacy knowledge treatments increase privacy concerns. • Only procedural knowledge positively affects perceived self-efficacy. • Self-efficacy and privacy concerns did not foster privacy management behaviors. • Instead, privacy fatigue predicts different privacy management behaviors over time. [ABSTRACT FROM AUTHOR]

Science and engineering education are mostly based on content assimilation and development of skills. However, to adequately prepare students for today's world, it is also necessary to stimulate critical thinking and make them reflect on how to improve current practices using new tools and technologies. In this line, the main motivation of this research consists in exploring ways supported by technology to enhance the learning process of students and to better prepare them to face the challenges of today's world. To this end, the purpose of this work is to design an innovative learning project based on collaborative work among students, and research its impact in achieving better learning outcomes, generating of collective intelligence and further motivation. The proposed collaborative working model is based on peer review assessment methodology implemented through a learning web-platform. Thus, students were encouraged to peer review their classmates' works. They had to make comments, suggest improvements, and assess final assignments. Teaching staff managed and supervised the whole process. Students were selected from computer science engineering at the University of Alicante (Spain). Results suggested greater content assimilation and enhanced learning in several scientific skills. The students' final grade exceeded what any student could produce individually, but we cannot conclude that real collective intelligence was generated. Learning methodologies based on the possibilities of Information and Communication Technologies (ICT) provide new ways to transmit and manage knowledge in higher education. Collaborating in peer assessment enhances the students' motivation and promotes the active learning. In addition, this method can be very helpful and time saving for instructors in the management of large groups. • Improved performance of students is produced through collaborative learning. • Student peer review enhances content assimilation. • An enhanced learning in several scientific skills is produced. [ABSTRACT FROM AUTHOR]

Research indicates that using social network sites as a source for news increases perceived knowledge even if, objectively, people fail to acquire knowledge. This might result from the frequent repetition of topics in news posts caused by multiple news outlets posting about the same news topics and the algorithm that favors similar postings. These repeated encounters can have a positive effect on the perception of knowing more, even if actual learning hardly occurs. An experiment (N = 810, representative of German Internet users) tested these assumptions. Participants were assigned to one of four groups and received a news feed with no information, few news posts, many news posts, or a full-length news article. Results indicate that many news posts increased perceived knowledge that is not paralleled by a gain in factual knowledge. Perceived knowledge mediates effects of reading many news posts on more extreme attitudes and the willingness for discussions. Even if participants who read the news article gained factual knowledge, they did not feel more knowledgeable than participants who were exposed to a news feed containing news posts. The results emphasize the meaning of engaging with full news articles, both for learning facts and for more accurate knowledge assessments. • Many news posts make people feel more knowledgeable than they are. • Only knowledge perception (not actual knowledge) is related to attitudes and behavior. • Many news posts indirectly affect attitudes and behavior through perceived knowledge. • News articles improve both actual knowledge and knowledge assessment. [ABSTRACT FROM AUTHOR]

Human-machine intellectual comparisons increasingly threaten the distinctiveness of humans. Drawing on social identity theory, we assume that people will compensate for the loss of human distinctiveness in a given area by valuing "alternative" human attributes (i.e., social creativity strategy). A preliminary study found that the defeat of a human go champion by an artificial intelligence threatened the rationality and refinement of humanity (i.e., threatened dimensions). Studies 1–3 found that participants primed with human-machine comparison, compared to controls, tended to evaluate alternative attributes such as emotional responsiveness (i.e., alternative dimensions) as uniquely human (Study 1) and as superior to machines (Studies 2–3). Finally, Study 4 found that the perceived loss of distinctiveness in threatened dimensions led people to evaluate the alternative dimensions as valuable for humanness. These findings suggest that people use social creativity to compensate for the loss of human distinctiveness by valuing the alternative dimensions under human–machine comparison. • Human-machine comparison threatens the distinctiveness of human identity. • The defeat of human go champion against an artificial intelligence threatened human attributes such as rationality. • As social identity theory predicts, people value non-threatened human attributes to compensate. • Four studies including experimental and survey methods replicated the result. [ABSTRACT FROM AUTHOR]

As online news today is an important source of political information and available in vast quantities, understanding its use and its impact on citizens' political knowledge is vital. The aim of the present study is to identify different usage patterns of online news and their relation to individually perceived (i.e., subjective) and actually measurable (i.e., objective) political knowledge. To do so, we conducted an online survey of German online news users (n = 396), investigating characteristics of their online news usage as well as their subjective and objective political knowledge. Latent class analysis revealed six distinct usage patterns, of which a usage pattern drawing heavily on social media and push accesses as a source of news (e.g., Facebook newsfeed, email newsletter) and being rather highly driven by entertainment needs has been found to be associated with an overestimation of one's own political knowledge (illusion of knowing). The potential negative implications of such an illusion of knowing are discussed against the backdrop of the democratic ideal of informed citizens. • Six online news usage patterns identified. • Identification based on motivations, news habits, and navigational styles. • Usage pattern drawing heavily on push accesses accompanied by illusion of knowing. [ABSTRACT FROM AUTHOR]

In the present research we analyzed the social influence mechanisms that back the relation between peer group norms regarding cyberbullying behaviors and individual cyberbullying perpetration. In a sample of adolescents (N = 3511, age: M = 16.27, SD = 1.58), we showed that the relation between perceived peer-norm and cyberbullying perpetration was moderated by two distinct social influence mechanisms. Specifically, when individuals' lack of knowledge regarding appropriate behaviors in cyberspace (i.e., cyberspace regulations), levels of perceived peer-norm regarding cyberbullying behaviors positively influence the participants' engagement in cyberbullying perpetration (i.e., informational social influence). Moreover, we showed that the higher the support of perceived peer-norm regarding cyberbullying behaviors the higher the levels of cyberbullying perpetration, especially for the higher (vs. lower) levels of identification with peers as the ingroup; this relation was additionally enhanced at increasing levels of adolescents' ingroup prototypicality (i.e., referential informative social influence). The results demonstrated that the two social influence mechanisms work independently and likely contribute to predict participants' engagement in cyberbullying perpetration. Results are discussed with respect to the current literature regarding the social influence mechanisms underlying cyberbullying. The implications of these findings for practical interventions are explored. • The study focuses on the relation between group-norms and cyberbullying perpetration. • The lower the level of cyberspace regulations the higher the levels of cyberbullying. • Ingroup identification moderates the relation between group-norms and cyberbullying. • Ingroup prototypicality moderates the relation between group-norms and cyberbullying. [ABSTRACT FROM AUTHOR]

Social media has been changing not only the way people communicate with friends, but also the way providers communicate with consumers. Being as the popular means of information acquisition and facilitating the exploration of various topics and market trends, social media was also playing a key role in the development of online to offline (O2O) commerce. In the current study, bilingual text mining was conducted to analyze the trends of O2O commerce from the perspective of social media. Keywords and their concept linking diagrams were first identified to understand the important O2O trends in different languages. While keywords "China", "Alibaba", "Baidu", "DianPing", "Meituan", "Asia", "ecommerce" and "service" were the most strongly associated English terms with O2O, keywords "Baidu", "Tencent", "headlines", "Sina.com", mode", "ecommerce", "retail" and "platform" were closely related to the Chinese tweets. To further understand the similarities and differences between English tweets and Chinese tweets, we compared the bilingual text mining results according to the categories of company, region, service Apps and operation model. Our study provided important insights from crowd intelligence and revealed trends analysis about the recent O2O development in different language regions. • Study provides insights from crowd intelligence and reveals trend analysis. • Bilingual text mining approach helps understand O2O development trends. • O2O ecommerce in China attracts public attention from different languages regions. • Companies apply approach to develop O2O strategy to enhance competitive advantage. [ABSTRACT FROM AUTHOR]

– In the recent year, with the emergence of various smart devices, the data is explosively increasing in the social Internet of Things (IoT) such as human healthcare. These data mainly involve information about user behaviors collected from various heterogeneous wireless sensor and social networks. Therefore, it is vital to analyze the data to find hidden meaning and convert it into valuable information. Due to the lack of capability to handle a wide range of queries, a traditional relational database provides inefficient analysis for the data. A graph database can easily store and analyze the data from various heterogeneous wireless sensor and social network using team formation algorithm. In the healthcare field, it is important to form a team that manages patients' health efficiently. The final goal of team formation is to organize experts who can perform task of data analysis. However, the existing team formation algorithms rely on a centralized computing environment and require high communication cost among experts to form a team. In this paper, we propose a parallel team formation method on apache spark (PTFS) to analyze graph data considering the crowd intelligence capability that exists in the graph data and social network. The PTFS employs two computation stages - a find skill and a merger subgraph and provides the parallel execution of many map tasks of graph data analysis. The experimental evaluation of the proposed method on a graph dataset demonstrates that it minimizes the communicating cost of the team members to form an optimized expert team in which a desired skill set is assigned to accomplish the graph data analysis. • Study of big data processing in the human healthcare. • Propose a parallel team formation method on apache spark (PTFS) to analyze graph data. • PTFS minimizes the communicating overhead of the team members. • Experimental evaluation of PTFS with a continuous graph dataset. [ABSTRACT FROM AUTHOR]

Evaluation on urban commercial centers' attractiveness not only benefits business strategy making and location choice, but also helps traffic management and urban planning. Traditionally, it is studied using questionnaires and field research, which are labor-intensive and time-consuming. To overcome these problems when evaluating the urban commercial centers' attractiveness, massive data analytics with datasets from taxi traffic, population, area, and road networks are adopted in this paper. Taking fifteen commercial centers at Shenzhen as a case study, a Cyber-Physical-Social System is built up to deal with these massive data for statistical analysis. An "Attractiveness Degree Model" is proposed to describe the degree to which customers desire to visit a commercial center. Then attractiveness thematic maps are drawn. Results show that YiTianJiaRiGuangChang has the highest attractiveness degree even though it has a small size and low commercial value. The attractiveness degree rankings are corroborated by annual customer satisfaction survey from Shenzhen Retail Business Association. Attractiveness thematic maps show that about 50–65% visits by taxis are within 5 km range. These results can be applied to support market analysis, urban planning, traffic management, and related areas. • Commercial center is precisely defined geographically and sociologically. • Attractiveness degree is the proposed metric to evaluate the attractiveness. • A Cyber-Physical-Social System is implemented for massive data processing. • Statistical analysis and thematic maps shed new insights into attractiveness. • Commercial centers are classified into four representative types in China. [ABSTRACT FROM AUTHOR]

This study aims to develop a better understanding of the potential drivers of personal social media use for work and the work-related information employees share through these platforms. This is important given both the increased use of social media as a form of self-presentation and the potential organizational consequences of workers' online knowledge and information sharing behaviors. The results draw upon a multi-method design combining survey data with content analysis data of workers' personal Twitter accounts. Findings indicate that organizational identification and a desire to succeed are related to the sharing of two distinct types of work-related content on social media: Organizational information sharing and professional information sharing. This research extends our understanding of how workers might use social media to communicate to audiences in different ways, and how this activity is related to their perceived relationships to organizational and professional groups. • Professional information sharing on Twitter is driven by a desire to succeed. • Organizational information sharing on Twitter is driven by identification. • The type of information workers share depends on their relationship to organizational and professional groups. • Public social media are hybrid spaces were professional and organizational information is shared. [ABSTRACT FROM AUTHOR]

Research traditions studying knowledge conceive of it differently and place boundaries between knowledge types in different places. A first challenge is elaborating a framework that distinguishes between the co-elaboration of knowledge types (cognitive, linguistic, social, technological...), yet shows their connections. A second challenge is distinguishing co-elaboration of knowledge from self and social regulation of knowledge elaboration. Knowing what knowledge you are regulating may facilitate knowledge regulation. Meeting the first challenge allows us to combine research from different traditions in order to broaden understanding. Meeting the second helps determine how to support the regulation of knowledge elaboration. As a first step towards meeting these challenges, we propose 1) the logical space of knowledge elaboration and its regulation and 2) a multi-theoretical, interdisciplinary model called Multi-grain collaborative knowledge construction that concerns how individuals and groups co-elaborate knowledge during group interactions. We instantiate this model with two illustrations (physics learning and collaborative game learning) and their corresponding logical spaces of elaboration and regulation. Results reveal how knowledge regulation occurs through action and not just verbalization, that regulation interventions are multifunctional, that meta knowledge about learning can help regulation, that knowledge acquired during development is more difficult to regulate. A roadmap is proposed for furthering the distinction between knowledge elaboration and regulation. • Regulation of knowledge occurs not only through verbalization, but also through action. • Regulation interventions can be multifunctional, targeting for example both cognitive and interactional competencies. • Regulation interventions can be multifunctional by being both elaboration and regulation. • Meta knowledge about the learning process that is specific to content can help regulation. • A roadmap is proposed for distinguishing between knowledge elaboration and regulation, for individuals and groups. [ABSTRACT FROM AUTHOR]

Learning maps allow learners to organize and personalize their learning materials, thus helping them to more effectively achieve their learning objectives. Accordingly, there has been ongoing research about learning maps with the goal of developing a comprehensive, easy to use, and powerful learning map. At present, the two most frequently used maps have either an ontological basis or take their design from the Petri net. These both provide a useful learning tool for learners. The ontology-based learning map represents integral concepts of knowledge and the relationships among concepts; however, it lacks the ability to control the learners' progress. The Petri net-based map can handle and personalize learning progress and designing the map is relatively easy, but its representation of the subject matter is relatively weak. The aim of this study is to design useful learning sequences and a representation interface that combine the above strengths. To do this, it offers the Dynamic Learning Paths Framework (DLPF), which is based on schema theory and the concept of collective intelligence. With the DLPF system, learners can provide feedback and contribute to a specific learning schema by submitting extra learning material. The self-improvement mechanism in the DLPF is designed to maintain the quality of learning materials to avoid the bias of collective intelligence. To evaluate the DLPF, questionnaires were developed and experiments to ascertain learning performance experiment were conducted. The results show that the proposed framework provides the well-organized learning materials, presents subject material effectively and can contribute to an improvement in learners' academic performance. • Record learning status and also present the knowledge structure in a learning map. • Collective intelligence concept for collecting and utilizing domain knowledge in an e-learning platform. • Automatic approach of learning materials lifecycle and merging process. [ABSTRACT FROM AUTHOR]

Online health support forums utilize straightforward online discussion designs to create a sociotechnical space where people can seek social support from others. The advice generated in these forums exists as an archival resource for future health information seekers. The present study uses mixed methods to investigate how invisible social processes lead advice to be adapted over time by forum members. Drawing on the construct of 'reification' from the communities of practice (COP) literature, we operationalize the reification of advice (RoA) as a process by which advice is developed across multiple discussion threads, and construct an algorithmic procedure to extract posts that trace this process. We evaluate our algorithm with crowd-workers, and perform an inductive, qualitative analysis to identify different modes of advice reification. We suggest that RoA could be used as the basis of a mid-level theory that treats online support communities and bundles of advice trajectories embedded in a shifting sociotechnical context. In our closing analysis, we propose that our approach might be a first step in an algorithmic procedure for assessing advice quality, drawing on the idea that reified advice may be considered a product of the collective intelligence of an online health support community. • Online health communities refine the advice they provide to newcomers over time. • This is similar to the reification of practice in a community of practice. • We introduce an algorithm to extract advice refinement arrayed across posts. • We identify three different types of advice refinement. • These different types reflect differences in the status of each type of advice for the community. [ABSTRACT FROM AUTHOR]

Internet addiction (IA) is prevalent and associated with negative measures of health functioning, with males appearing more vulnerable than females. However, little is known about gender-related differences in the effect sizes of IA globally. This multinational meta-analysis addresses this gap in knowledge by providing estimates of effect sizes of gender-related differences in IA tendencies across jurisdictions and how they relate to global national indexes including gross domestic product (GDP) per capita, internet penetration, gender-related gaps in economies, internet penetration, alcohol consumption, smoking prevalence, life satisfaction and suicide rates. One-hundred-and-one studies consisting of 115 independent samples involving 204,352 participants from 34 countries/regions were identified. The average effect size of gender-related differences in IA in a random-effects model was small at g = 0.145. The highest gender-related effect sizes were in Asia with g = 0.208, and the lowest were in the North America with g = −0.049, Africa with g = 0.092 and Europe with g = 0.114. Meta-regression revealed that smaller effect sizes were observed in nations with greater GDP per capita and higher internet penetration. As operationalized, both the internet availability and social norms hypotheses were supported by effect sizes being positively related to gender-related differences in economic measures, internet penetration, smoking prevalence and alcohol consumption. The Psychological Well-being hypothesis was largely not supported, since the effect sizes were unrelated to gender-related differences in life satisfaction and negatively related to gender-related differences in suicide rates. Findings suggest economic factors, internet availability, social norms and some addiction-related health factors may relate importantly to gender-related differences in IA tendencies across countries. Image 1 • Males have higher IA tendency than do females with a small effect size. • The largest effect size was found in the region of Asia. • Effect size was positively related to GDP per capita and internet penetration. • Internet availability and social norms hypotheses were supported. [ABSTRACT FROM AUTHOR]

This paper explores the influence of digital technologies on the consumer decision-making in retail sector with two online survey-based studies. Study 1 identifies unique attributes of six digital technologies, including two current (Internet and Mobile Platform) and four emerging (Artificial Intelligence, Augmented, Mixed and Virtual Reality) technologies. Study two focuses on older consumers to understand their decision-making process when shopping for products or services using new digital technologies. We extend the AISAS (Awareness, Interest, Search, Action, and Sharing) model to show that with digital technologies, consumer decision journey is no longer linear. For example, attention can lead directly to action, without going through the interest or search stages. Similarly, purchase can lead to sharing that may lead to loyalty and psychological engagement, and reinforce attention. We found no significant difference in these effects between older and younger consumers. Besides providing useful insights about consumer decision-making process with emerging digital technologies for future academic researchers, these results also give useful ideas to marketing practitioners interested in introducing these emerging technologies to deliver superior value to their customers. • Emerging digital technologies are changing consumer decision-making process. • We explore this phenomenon in retail shopping context using two online studies. • Study one identifies and categorizes unique attributes of digital technologies. • Study two confirms non-linear consumer decision-making with digital technologies. • No influence of consumers' age on their decision-making with digital technologies. [ABSTRACT FROM AUTHOR]

In the realm of social media influencer advertising, the organic nature of sponsored content can deceive users by concealing persuasive intent. Regulators seek to reinforce users' persuasion knowledge through sponsorship disclosure guidelines, while advertisers explore ethical tactics to prevent the adverse effects of users' ad recognition on the effectiveness of influencer endorsements. With the increasing prevalence of image-based content, the investigation of visual factors, such as the presentation of products, and their moderating influence on the effects of ad recognition becomes ever more relevant. However, previous research has primarily focused on textual factors. Building on congruence theory, we demonstrate that marketers can harness bottom-up attention induced by high visual salience to counteract the detrimental effects of users' ad recognition. Our findings indicate that ad recognition undermines favorable brand attitudes, word-of-mouth intentions, and purchase intentions by diminishing perceived influencer integrity and enhancing post skepticism. These effects are alleviated when the visual appearance of sponsored products is highly salient in terms of brightness or color. Our work offers a simple yet effective image-based solution for marketers to mitigate ad-related skepticism and elevate the persuasive potential of influencer advertising in the visually-driven realm of computer-mediated communication. • Visual salience of products mitigates the negative effects of users' ad recognition on influencer and endorsement evaluations. • Changes in perceived influencer integrity explain the increase in post skepticism resulting from enhanced ad recognition. • Visual salience of products drives bottom-up attention to products in social media images. [ABSTRACT FROM AUTHOR]

Multiple unmanned system control is a complex command and control endeavor but pairing human operators with an intelligent agent (IA) teammate can buttress the collection and synthesis of data and improve complex decision making. Effective human-autonomy teams (HATs) require human trust in IA teammates to be properly calibrated, which can be supported by communications pertaining to underlying functions of the IA, or "transparency". One prominent guide for application of transparency is Chen and colleague's Situation awareness-based Agent Transparency (SAT) model. This effort sought to extend understanding of the application of this model by manipulating secondary transparency communication parameters: face threat (i.e., threat to a person's sense of social standing) and design of transparency communication (verbal, graphical, and iconographical). Results revealed that increasing face threat can improve reliance calibration at low transparency but may be detrimental when transparency is high. Outcomes concerning the method of transparency communication suggest that while verbal communication of transparency information is sufficient and even preferred when a low level of transparency is provided, reliance on graphical and iconographical approaches for presenting transparency information increases at a higher level of transparency. • Face threat increased performance and reliance when transparency was low but was detrimental when transparency was high. • Face threat increased trust with low transparency but nullified trust increases typically seen with higher transparency. • Face threat affected operator task performance strategy and collaboration style even without changes in subjective trust. • Verbal communication of transparency information is preferred when low levels of transparency are provided. • Graphical communication of transparency information may be more useful as amount of transparency information increases. [ABSTRACT FROM AUTHOR]

Earlier research provides inconsistent evidence for effects of pedagogical agents on learning outcomes. We claim that the agent's instructional method helps to explain the inconsistency, with agents that offer behavioral modeling as an instructional method leading to better learning. We conducted two studies to investigate effects of the use of an artificial agent as a persuasive behavioral model on individuals' learning outcomes in the context of computer training. Specifically, in both studies, participants watched an instructional video on how to perform a web search with their eyes using a novel eye-tracking software. Study 1 examined the effects of agent-delivered modeling vs. two non-modeling instructional methods (agent-delivered instructional narration and no agent, text-only instruction) on participants' (N = 197) self-efficacy and system-specific perceptions of ease of use. Study 2 extends findings of Study 1 by examining effects of agent-delivered modeling vs. two non-modeling instructional methods (agent-delivered instructional narration and no agent, voice-only instructional narration) on participants' (N = 99) declarative knowledge and task performance. Previous work with human behavioral models showed an advantage of behavioral modeling over other non-modeling instructional methods in influencing learning outcomes. Therefore, agent-delivered modeling was predicted to be more effective in influencing motivational (i.e., self-efficacy) and learning outcomes (i.e., declarative knowledge, task performance). In accordance with our hypotheses, results revealed that participants who received instructions from an artificial agent as a behavioral model reported significantly stronger self-efficacy beliefs, tended to have higher system-specific ease of use, exhibited enhanced declarative knowledge, and better task performance skills. • An artificial agent as a behavioral model increases learners' self-efficacy and system perceived ease of use. • An artificial agent as a behavioral model enhances learners' task performance. • An artificial agent as a behavioral model can lead to better declarative knowledge. [ABSTRACT FROM AUTHOR]

Analyzing multimodal multichannel data about self-regulated learning (SRL) obtained during the use of advanced learning technologies such as intelligent tutoring systems, serious games, hypermedia, and immersive virtual learning environments is key to understanding the interplay among cognitive, affective, metacognitive, and social processes and their impact on learning, problem solving, reasoning, and conceptual understanding in learners of all ages and contexts. In this special issue of Computers in Human Behavior , we report six studies conducted by interdisciplinary teams' use of various trace methodologies such as eye tracking, log-files, physiological data, facial expressions of emotions, screen recordings, concurrent think-alouds, and linguistic analyses of discourse. The research studies focus on how these data were analyzed using a combination of traditional statistical techniques as well as educational data-mining procedures to detect, measure, and infer cognitive, metacognitive, and social processes related to regulating the self and others across several tasks, domains, ages, and contexts. The results of these studies point to future work necessitating interdisciplinary researchers' collaboration to use theoretically based and empirically derived approaches to collecting, measuring, and modeling multimodal multichannel SRL data to extend our current models, frameworks, and theories by making them more predictive by elucidating the nature, complexity, and temporality of underlying processes. Lastly, analyses of multimodal multichannel SRL process data can significantly augment advanced learning technologies by providing real-time, intelligent, adaptive, individualized scaffolding and feedback to address learners' self-regulatory needs. [ABSTRACT FROM AUTHOR]

Abstract This descriptive study sought to understand the complexities of integrative processing during collaborative online reading. Student pairs constructed a collaborative understanding while reading online information about a controversial issue by connecting, combining and organizing information that originated from prior knowledge, self-selected online texts, and discussions during an online inquiry task. Thirty-eight students from an upper secondary school in Finland worked in pairs to read online information and write an essay with the help of an argument graph tool. Primary data sources consisted of: prior knowledge; discussions; notes recorded with a graphic representational tool; video capture files of online use; and essays. The following results emerged: 1) a methodology and a taxonomic system were developed for the study of information sources involved in collaborative synthesis; 2) the integration of ideas from multiple online texts was difficult for adolescent students; 3) students with better essays used more online information whereas students with less remarkable essays relied more on prior knowledge that was activated during online reading. The methodology used in this study provides initial direction for research on the complexities of synthesis during collaborative online reading, an increasingly important aspect of learning in schools. Limitations and future directions for research are discussed. Highlights • Collaborative synthesis combines prior knowledge, online texts and discussions. • Collaborative synthesis is complex and differs during initial and later stages. • Intertextual integration during online reading was difficult for upper secondary school students. • Students with high-quality essays relied more on online texts. • Students with less remarkable essays relied more on their prior knowledge. [ABSTRACT FROM AUTHOR]

Abstract Researchers often propose that seeking critical, constructive feedback is due to a growth mindset. However, there is no evidence that mindset is related to feedback-seeking behaviour. This study examines for the first time the relation between mindset and feedback-seeking behaviour; moreover, it is the first time when this topic is examined in a sample of pre-service teachers. A number of n = 68 pre-service teachers at a North American university designed posters in an online game that assessed their learning choices (willingness to seek critical feedback and to revise posters) and poster performance. Then, they completed an online mindset survey regarding their ability to design posters. Results show that growth mindset does not correlate with feedback-seeking choices, revising choices, and poster performance. However, growth mindset moderates the relation between performance and learning choices (critical feedback and revising): individuals endorsing higher levels of growth mindset designed significantly better posters only when choosing higher levels of critical feedback or revision. Impact and implications Results reveal two novel theoretical distinctions: (1) this first examination of the relation between mindset and learning choices shows that growth mindset does not drive learning choices, but it seems to drive pre-service teachers' response to critical feedback; (2) fixed and growth mindset seem to be distinct mindset constructs, as they have different effects. As the influence of critical feedback on performance is moderated by pre-service teachers' differential endorsement of growth mindset, implications include interventions and teaching practice that inculcate a growth mindset and a critical feedback-seeking attitude in pre-service teachers. These interventions are necessary, as most learners would not know that constructive criticism is more beneficial for learning than praise, regardless of the mindset they endorse. Highlights • This study examines for the first time the relation between mindset, feedback-seeking, and performance. • It is the first time when this topic is examined in a sample of pre-service teachers. • Results show that growth mindset does not drive pre-service teachers' learning choices. • Also, growth mindset drives pre-service teachers' response to critical feedback. • Growth mindset moderates the relation between learning choices and outcomes. [ABSTRACT FROM AUTHOR]

Abstract In this work, we explore the effects of dominant nonverbal behavior of a virtual agent on users in different age groups regarding their evaluations of the system as well as persuasion. We report a bipartite experimental laboratory study with young adults (N = 87) and seniors (N = 38). In the first part of the study, young adults interacted with the virtual agent in a desert-survival-scenario-task in a 2 (dominant vs. submissive nonverbal behavior) x 2 (no attention guiding behavior vs. attention guiding behavior) experimental between-subjects design. Besides scrutinizing the effects of dominant nonverbal behavior, we wanted to know how adding nonverbal behavior to guide the users' attention influences the perception of dominant nonverbal behavior. While the dominant agent was indeed perceived as being more dominant, evaluations of the agent regarding likability and intelligence was not influenced by its nonverbal behavior. There were some effects for the perception of the game, however, the dominant behavior was not more successful regarding persuasion (following the agents suggestions during the game). Adding attention guiding nonverbal behavior did only result in lower perceived competence of the agent. In the second part of the study, we replicated the submissive/dominance manipulation with senior participants in order to explore age differences. We found a number of effects: seniors evaluated the agent generally more positively (e.g., more likable, autonomous, intelligent, submissive, verbally and nonverbally capable). Moreover, we found an interaction effect for persuasion, showing that seniors strongly follow the suggestions of the dominant agent. We discuss these findings especially against the background of agent applications in a medical context. Highlights • We explored the effect of an agent's dominant nonverbal behavior on its persuasiveness. • Interfering attention guiding behavior did amplify dominance perceptions for the dominant agent. • A dominant agent more strongly persuaded seniors than a submissive agent. • Dominant behavior did not lead to stronger persuasive effects for young adults. • Seniors generally evaluated the agent more positively than young adults. [ABSTRACT FROM AUTHOR]

Abstract Many emerging technologies, such as our smartphones and computers, extend the capabilities of our cognitive systems by serving as external memory and sources of information. Despite such positive impacts, the increasingly ubiquitous human-computer partnership may fundamentally alter the ways in which humans perceive and evaluate themselves. This study examines features of technological devices that influence evaluations of personal knowledge (i.e., knowledge "in the head"). In two experiments, participants evaluated their cognitive abilities after using a technological device to find answers to trivia questions. In Experiment 1, participants who retrieved answers from the internet reported higher cognitive evaluations compared to participants who could not use the internet. Routine use of the device used to access the internet moderated this effect. In Experiment 2, participants who used their own device to find answers reported higher cognitive evaluations compared to participants who used a control device, and participants who used a smartphone reported higher cognitive evaluations compared to participants who used a laptop. We conclude by highlighting several variables emanating from both the sources present through the device interface (e.g., search engine), and the physical manifestation of the device itself, which may contribute to illusions of internal knowledge through technology use. Highlights • Using technology to retrieve information potentiates illusions of internal knowledge. • Features of a device play an important role in influencing metacognitive evaluations. • Feelings of familiarity with an external source or source features contribute to elevated metacognitive evaluations. [ABSTRACT FROM AUTHOR]

Concept mapping has been widely used to foster meaningful learning and support the communication of complex ideas. With a focus on conceptual understanding, traditional concept mapping is found to be inadequate in supporting problem solving particularly in eliciting and representing the complex process of applying knowledge to practice. In this study, a computer-based cognitive-mapping approach was used to extend traditional concept mapping by allowing learners to represent the problem-solving process and the underlying knowledge in a visual format. By representing ideas both verbally and pictorially, the cognitive mapping approach has a high potential to foster effective thinking and reflection in problem-solving contexts. This study examined the effects of the computer-based cognitive-mapping approach by comparing it to a note-taking approach that represents ideas in verbal text only. Forty-nine senior year medical students participated in the study. The experimental group used the cognitive-mapping approach, while the control group used a note-taking approach, to articulate complex thinking and actions when working with simulated clinical diagnostic problems in a computer-based learning environment. The results show the promising effects of the cognitive-mapping approach on improving students' problem-solving performance, subject-matter knowledge, and intrinsic motivation to learn with complex problems. [ABSTRACT FROM AUTHOR]

This paper extends the investigation of deeper learning beyond learning as an individual endeavor by looking at the collaborative achievement of teams. Healthcare students were engaged in collaborative concept mapping to solve an authentic, contextually situated problem in triads. Both quantitative and qualitative investigations have been conducted to study specifically the impact of learners' conflict resolution on their deeper learning as measured by knowledge convergence (KC). Knowledge convergence refers the similarity between team members' post-collaboration knowledge representations derived through interaction. To compute learners' KC, team members' post-collaboration individual maps were compared through Pathfinder analysis. Contrary to prior research findings, increased conflict negotiation behaviors by an individual resulted in fewer similarities between his or her second map and other members' maps. Theme analysis revealed both effective interaction strategies and challenges that impacted triad team members' attainment of KC. Findings from this study can inform development of effective instructional support to cultivate the occurrence of deeper learning within the contexts of collaborative concept mapping and healthcare education. [ABSTRACT FROM AUTHOR]

Marketers recognize digital celebrities' advertising power and effectiveness, making them influential models of social media advertising. The concept of parasocial relationships may elucidate relationships between digital celebrities and their followers and explain the former's persuasion power over the latter. This study explores how parasocial relationships affect followers' purchase and electronic word-of-mouth (eWOM) intentions and investigates the effect of empathy, loneliness, and low social self-esteem on parasocial relationships within Uses and Gratification Theory. Additionally, it examines whether followers' parasocial relationships can reduce their persuasion knowledge. A survey was administered three hundred eighty-nine Chinese SNS users following digital celebrities; valid data were analyzed using structural equation modeling. Empathy and low self-esteem positively influenced parasocial relationships; however, loneliness did not. Parasocial relationships positively affected followers' purchase and eWOM intentions. The second model investigated the negative effects of persuasion knowledge on followers' purchase and eWOM intentions. The results indicate that parasocial relationship moderates the paths between followers' persuasion knowledge and purchase intentions and between followers' persuasion knowledge and eWOM intentions. This study sheds light on how digital celebrities have an advertising influence on their SNS followers with the concept of parasocial relationships and is notable given the dearth of empirical studies on digital celebrities’ SNS advertising. [ABSTRACT FROM AUTHOR]

Considering both personal and social perspectives, self-identity and reciprocity were analyzed to identify the underlying mechanisms used by opinion leaders to accumulate their social interaction ties in virtual communities. As a key factor in maintaining the operation of virtual communities, knowledge contribution is employed in our route model. An online survey conducted in several different virtual communities, yielded 666 useable responses. The research findings indicate that opinion leaders accumulate their social interaction ties through different routes such as self-identity, knowledge contribution, and reciprocity. We also observe that both knowledge contribution and reciprocity have a mediating effect on the relation between opinion leader status and social interaction ties. In addition, knowledge contribution has a mediating effect on the relationship between self-identity and social interaction ties; and on the relationship between reciprocity and social interaction ties. The indications and implications of our findings, as well as the limitations of our study, are discussed. [ABSTRACT FROM AUTHOR]

This research investigates customers' reactions to technological products and argues that due to different perspectives on implicit theories of intelligence, people would have different reactions to these products framed as complex versus simple. In two experiments, product complexity is manipulated by including some steps or processes to use a product (i.e., complex) versus minimum steps to use a product (i.e., simple). The results indicate that although product complexity has a negative impact on product interest, customers' beliefs about the degree of malleability of their intelligence can interact with product complexity, thereby impacting their reactions to complex, technological products. In particular, the results indicate that for a technological product perceived as complex, incremental theorists have more favorable reactions than entity theorists, while for a technological product perceived as simple, incremental and entity theorists have similar reactions. Further, this research finds that individuals low in negative effort beliefs react more favorably to complex products, compared to those high in negative effort beliefs. This research concludes with theoretical and managerial implications and offers some directions for future research. [ABSTRACT FROM AUTHOR]

As the demand for automatic video interviews powered by artificial intelligence (AI) increases among employers in the postpandemic era, so do concerns over job applicants' trust in the technology. There are various forms of AI-based video interviews with and without the features of tangibility, immediacy, and transparency used for preemployment screening, and these features may distinctively influence applicants' trust in the technology and whether they engage in or disengage from the hiring process accordingly. This field study involved designing a test of the effect of various forms of AI-based video interviews on interviewees' cognitive and affective trust based on the self-reporting of 152 real job applicants. The study found that AI used in asynchronous video interviews (AI-AVI) increased applicants' cognitive trust from that in the non-AI condition. Moreover, when the AI-AVI had features of tangibility and transparency, the applicants' cognitive and affective trust increased. However, the feature of immediacy did not have a statistically significant impact. Contrary to concern over the potential negative effects caused by AI and its features, no statistically significant impacts were found in this study. • The AI used in asynchronous video interviews (AVI) increased interviewees' cognitive trust. • AI-based AVI with tangibility increased interviewees' affective trust. • AI-based AVI with transparency increased interviewees' cognitive trust. • AI-based AVI with immediacy did not affect interviewees' trust. • AI-based AVI with or without the above features did not impair interviewees' trust. [ABSTRACT FROM AUTHOR]

Online games are prominent computer applications worldwide. Gamers form teams to cooperate to achieve gaming goals. If gamers do not have in-game cooperation knowledge, they may find it difficult to integrate into gaming groups (e.g., teams and communities), which may further undermine their loyalty toward online games. Nevertheless, little is known about how in-game cooperation knowledge forms online gamer loyalty, indicating a research gap. This gap hinders game makers from effectively allocating resources to strengthen gamer loyalty. Based on social capital theory, we develop a model to fill this gap. We collected 3036 responses from online gamers and used hierarchical regression to analyze the data. We find that cooperation knowledge, team participation experience, and gaming community identification are positively related to online gamer loyalty. Moreover, cooperation knowledge strengthens the relationship between team participation experience and loyalty. Overall, our findings uniquely inform game providers that gamers' cooperation knowledge not only directly strengthens gamer loyalty but also amplifies the positive impact of team participation experience on loyalty. • We examine how in-game cooperation knowledge formulates gamer loyalty. • Such knowledge, team participation, and community identification fuel loyalty. • Such knowledge strengthens the link from team participation to gamer loyalty. [ABSTRACT FROM AUTHOR]

There are grounds to assume that the use of nonpolitical, entertainment-oriented Social Media (SM) may dampen democratically relevant outcomes. However, research has largely ignored the political effects of such entertainment-oriented SM content as well as its interaction with exposure to political SM content. Based on the distinction between political and entertainment-oriented SM use, we developed a fourfold theoretical typology, "the Inactive", "the News Avoiders", "the Distracted", and "the Focused". Using data from a two-wave panel study (N = 559), we found that "the Focused" scored highest while the "the Inactive" and the "the News Avoiders" scored lowest on democratically relevant outcomes. Autoregressive panel analyses further revealed a positive effect of political SM exposure on low-effort political participation, but not on high-effort participation, political interest, and knowledge over time. Exposure to entertainment-oriented content on SM was associated with a decrease in high-effort political participation over time. For low-effort participation and political interest, the over-time effect of political SM exposure was dampened with rising levels of entertainment-oriented SM exposure, suggesting a distraction effect. Implications are discussed. • Research on social media and democracy ignored non-political content. • Entertainment-oriented content on social media can have distraction effects. • Entertainment-oriented content reduces the likelihood of high-effort participation. • Political and entertaining content interact when shaping low-effort participation. • SNS are not universally beneficial to democracy. [ABSTRACT FROM AUTHOR]

This paper investigates the complementary or substitute relationship between knowledge searching in teams and in professional virtual forums (PVFs) will depend on the motivations of knowledge searching. We select creative thinking as an intrinsic motivation of knowledge searching in teams and expertise identification as an extrinsic motivation of employees' knowledge searching in teams in order to do creative work. Using the sample of 201 professional employees in Chinese stock service companies, our results demonstrate that when employees are driven by creative thinking, their searching knowledge in PVFs will decrease their searching in teams, which is a substitute effect. When employees are driven by expertise identification, employees' searching knowledge in PVFs will not influence their searching in teams. These findings extend extant knowledge searching and creativity literature by taking employees' knowledge searching motivation and knowledge searching in PVFs into the consideration. Theoretical and practical suggestions are discussed. [ABSTRACT FROM AUTHOR]

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks. [ABSTRACT FROM AUTHOR]

This study investigated the Dunning-Kruger Effect in the domain of workplace computing. The Dunning-Kruger Effect is present in situations where people with lower levels of skill or knowledge in a domain do not recognise their actual level of skill. Participants in this study were asked to make an estimate of the size of the domain of end-user computing as it relates to the greater domain of computing as a whole. It should be noted, that although end-user computing is present in many situations, on the whole its undefined nature is unappreciated by many users. A measure of perception was used as a way of investigating if people, who appreciate the extent of a domain, will be more conservative in their estimations of it, their knowledge in it and that same knowledge of an average person or peer. Results indicate that the Dunning-Kruger Effect is present in workplace end-user computing in situations where self-assessments are based on what a person believes there is to know in a domain. However, this effect was not present in situations where participants made estimations of the knowledge of workplace end-user computing of an average end-user. In this case, these estimations appeared to be based on what a person believed they knew not what they believed there was to know. Technology is advancing at a fast rate and end-user computing applications are not immune to this change. This study is important as it highlights a domain, which, due to its fast-changing nature, is near impossible to define but one that is critical to modern workplaces. The study shows that neither employers nor employees currently enough of an appreciation of a domain to understand its extent and how misinterpreting skill level can pose challenges to both groups. [ABSTRACT FROM AUTHOR]

Gamification aims to implement game features in non-game contexts, with the goal of increasing the motivation of individuals performing a specific task or set of tasks. The tasks themselves, can focus on cognitive behavior change (e.g., overcoming anxiety) or physical behavior change (e.g., overcoming a shoulder injury). Current gamification methods primarily serve as guidelines and principles for the design of gamified applications. Moreover, these methods often overlook the complexity of actually implementing the game features and do not consider the effects that game features have on individuals' ability to perform a target task . A knowledge gap exists in understanding the tradeoffs between the complexity of implementing a game feature and the impact it has on increasing individuals' motivation and performance on a particular task or set of tasks. This paper presents a method for evaluating the complexity of implementing game features and the physical effort required to perform the tasks of the application, with a specific focus on physically-interactive gamified applications. Designers will gain a fundamental understanding of how the implementation of specific game features, contributes toward the objective of the application. A case study is presented that focuses on physically-interactive gamified applications in a virtual environment. Empirical results measuring the effects of game features on participants' performance are presented, which provide evidence in support of the metrics proposed in this study. Knowledge gained from this work will inform designers on how to manage their resources more efficiently and predict possible design issues (e.g., not meeting the objective of the application) while creating gamification applications. [ABSTRACT FROM AUTHOR]

Serious or educational games gain increasing research interest as tools to augment traditional instructional approaches on scholastic learning, especially in mathematics education. In this study, we investigated whether game-based approaches may not only be useful to foster numerical learning but may also be valid as an assessment tool. To measure their conceptual knowledge of fractions eleven-year-old students played a math game on tablet computers using tilt-control to navigate an avatar along a number line for a total of 30 min. Findings indicated that hallmark effects of fraction magnitude processing typically observed in basic research, such as the numerical distance effect, were successfully replicated using the game-based assessment. Moreover, fraction comparison performance as well as fraction estimation accuracy correlated significantly with students' math grades. Therefore, the results of the current study suggest that game-based learning environments for fraction education (even using tilt-control) may also allow for a valid assessment of students’ fraction knowledge. [ABSTRACT FROM AUTHOR]

The paper proposes an approach of enhancing knowledge workers'employability by inducing positive behavioral changes with the aid of current social and semantic technologies. Employability is seen as a psycho-social construct, connected to the development of knowing-how competencies and knowing-whom competencies, by continous learning and a rich social environment. The central element of the approach is a web-based platform - EmployLeaP, which facilitates adults' leap towards a better professional life and gives them the opportunity to find suitable professional e-communities, in which they can learn from their peers and feel appreciated. The recommendation algorithm for finding e-communities is ontology-driven and exploits information retrieved from social connectors anchored in the existent online learning ecosystem. In the same time, during learning within EmployLeaP communities, users' professional visibility is enhanced, by recommending them to job recruiters who also join the platform, in searching competent workers. The badge system used to reward exceptional behaviour within e-communities is the one ensuring users' visibility to recruiters and, in the same, increasing their self-confidence. Preliminary data related to experimental exploitation and perceived usefulness of the platform show promising results of using recent technologies in assisting individuals who want to increase their employability, as well as other stakeholders of knowledge society – recruiters or knowledge organizations. [ABSTRACT FROM AUTHOR]

Continued growth in older adults' computer and internet use has led to the need to better assess their competencies and skills. The aim of the current study was to expand on this literature by examining sources of individual differences in older adults' computer and internet proficiency. Ninety-seven adults ranging in age from 60 to 95 completed the Computer Proficiency Questionnaire (CPQ) along with a battery that measured demographic information, socio-emotional variables such as sense of control and affect, and cognitive abilities such as reasoning and speed of processing. Hierarchical regression analyses examined the predictors of CPQ Total score as well as the three CPQ subscales (e.g., Internet and Email Use, Communication and Calendaring, and Computer Basic). Age, education, affect, sense of control, inductive reasoning, perceptual speed, and psychomotor speed were associated with at least one domain of computer proficiency. Positive affect uniquely predicted Communication and Calendaring. While sense of control, inductive reasoning, and psychomotor speed uniquely predicted Computer Basic. Discussion focuses on implications for CPQ use and computer proficiency training in older adults. [ABSTRACT FROM AUTHOR]

The popularity of multiplayer online battle arena (MOBA) games provides an opportunity to examine how cognitive skills are linked with changes in player performance within a team-based game genre. There is evidence that video game experience in other genres is connected with specific cognitive skills such as visuospatial attention and working memory capacity. Links have also been observed outside of video games such as numerical intelligence being correlated with the performance of chess experts. In the present study, we investigated whether numerical and working memory ability measured at an initial time-point predicted changes in MOBA skill and gameplay frequency measured five months later. We observed that participants who scored higher on a number processing task had greater improvements in MOBA skill and that this link depended on the social context of the gameplay. Specifically, numerical skill was linked to player rankings generated from matches where they were paired with random individuals; no connection was observed with gameplay that took place with a familiar set of teammates. The results of the present study extend previous research indicating video gameplay is linked to specific cognitive skills, in the case of MOBAs being numerical processing. We suggest that the difference in the connection with number ability to gameplay with novel and familiar players is in line with evidence that the importance of individual performance and the sense of achievement can be reduced in team settings. We suggest that future research should examine what types of quantitative abilities are indicative of MOBA skill and the impact of those factors relative to social skills. [ABSTRACT FROM AUTHOR]

Increasingly, our understanding of what smart cities and human-computer behavior represent and embody, is shaped by our first-hand experience of both. Whereas there is a certain commonsensical promise in it, in that -- since we live it -- we 'ought' to be able to understand more and explain more about the related phenomena, the challenge that arises is that we do not necessarily see more. Accordingly, whichever story about the world, in this case about smart cities and/or human-computer behavior or combinations of them, we may wish to tell, each of them will bear the mark of our prior knowledge, experiences, and worldviews. Not always will it expand and deepen the exploratory and explanatory potential of the ensuing research.The papers included in this special issue seek to navigate this challenge. [ABSTRACT FROM AUTHOR]

Disinformation currently floods the Internet worldwide and likely affects social knowledge building in online communities. Strategic framing in the form of emotional, value, and semantic framing is a common tool fake news producers use to more efficiently disseminate their content, yet these strategies have not been sufficiently examined, even less in relationship with the online dialog initiated by misinformation. In this exploratory study we aim to investigate the most relevant types of strategic framing and their role as predictors of news consumers' emotions, argumentation, and social knowledge building in the online dialog on a German alternative news site. Employing both manual and automated content analysis, we found significant relationships between framing in posted news articles and the subsequent online dialog. News framing predicted negative emotions in the online discussions and interfered with argumentation and social knowledge building. Conclusions pertain to digital information literacy interventions and further research on news framing. • This study combines framing theory and dialogism with disinformation content. • We utilized both manual and automatic content analysis to discover prominent frames in fake news content. • We analyzed roughly 1500 comments from a German fake news site. • Framing predicted social knowledge building in the sense of dialogism and might have led to an echo chamber environment. [ABSTRACT FROM AUTHOR]

In the present study, we tested the common assumption that teachers with more experience consider themselves better prepared for online teaching and learning (OTL). Utilizing the data from a survey of 366 higher-education teachers from Portugal at the beginning of the COVID-19 pandemic in 2020, we performed structural equation modeling to quantify the experience-readiness relationship. The survey contained an assessment of teachers' OTL readiness which was measured by their perceptions of the institutional support, online teaching presence, and TPACK self-efficacy. In contrast to the linearity assumption "the more experienced, the better prepared", we found robust evidence for a curvilinear relationship. Teachers' readiness for OTL increased first and then decreased with more experience—this applied especially to the self-efficacy dimension of readiness. Further analyses suggested that the experience-readiness relationship does not only exist at the level of aggregated constructs but also at the level of indicators, that is, specific areas of knowledge, teaching, and support. We argue that both novice and experienced teachers in higher education could benefit from experience-appropriate, pedagogical, and content-related support programs for OTL. • Readiness for online teaching and learning (OTL) comprised multiple dimensions. • The relation between OTL readiness and experience was curvilinear. • Curvilinearity occurred especially for TPACK self-efficacy. • Experience impacted the measurement of readiness for OTL. • The nature of the relation was robust against outliers of experience. [ABSTRACT FROM AUTHOR]

Rapid growth in the reliance on teamwork in organizations, coupled with advances in artificial intelligence, has fueled increased use of Human Autonomy Teams (HATs) involving the collaboration of humans and agents to complete work. Although there are many successful examples of HATs, researchers and technology developers can see additional applications if agents were better able to understand the mental states of humans to anticipate what a team is likely to do next. Creating this capability requires the creation of models of team interaction that enable agents to interpret a team's current state and anticipate its future state. To build this model, we draw on research on collective intelligence (CI), which shows a team's capability to work together can be characterized by a latent collective intelligence factor, based on observations of work across a range of tasks, and which predicts a team's ability to accomplish a wide range of goals in the future. While some work uses a specific battery of CI tasks, more recent studies have identified observable collaborative process metrics that can be captured passively. Building on this work, we propose a method of evaluating CI by representing it as a latent variable represented by the hidden state in a Hidden Markov Model. The observations used as input to the model are the team's observable collaborative process behaviors (i.e., collective effort, use of task-related skills, and task-strategy efficiency). We show by learning the set of hidden states representing a team's observed collaborative process behaviors over time, we both learn information about the team's CI, predict how CI will evolve in the future, and suggest when an agent might intervene to improve team performance. Based on the model's observations, we discuss how it can help agents diagnose teamwork and possibly make interventions to improve CI by identifying areas of collaborative process (collective effort, skill use, or task strategy) that could be improved. • Hidden Markov Models can directly model how latent factor collective intelligence influences the behavior of human-agent teams. • Models of team collaboration help agents better understand teamwork and predict how team behavior will unfold over time. • HMMs and alternative models showed similar levels of predictive ability for future actions. • Future research will examine the role that emotion could further improve predictions of future team behavior. [ABSTRACT FROM AUTHOR]

This study of anthropomorphic response to artificial intelligence begins with an extensive review of the literature and an identification of conceptual distinctions between anthropomorphism and anthropomorphic response. The authors develop an instrument for measuring how users form anthropomorphic response to interactions with AI chatbots. Amazon MTurk is used to recruit 120 users for a pilot study and 303 users for the main study. Participants respond to six scenarios depicting interactions with banking service chatbots of varying appearance and intelligence. Results show that anthropomorphic response depend on perceptions of agent appearance, cognitive intelligence, and emotional intelligence. Users perceive more humanness in highly intelligent but disembodied agents rather than in highly intelligent agents that have poorly designed appearances. And users who have strong tendencies to anthropomorphize non-sentient entities are less likely to form anthropomorphic response when interacting with agents with high cognitive intelligence. The study enhances understandings about human/AI interactions. It provides directions for future research regarding anthropomorphic response and provide directions for future research on designing and using artificial agents. • Anthropomorphic response (AR) comprises anthropomorphic perceptions and behaviors. • AR involves both the AI agent's features and the user's features. • AR is built on the appearance and the intelligence of an AI agent. • Users' tendency interacts with the AI agent's appearance and intelligence. • AI agents with no appearance result in higher AR than with poor appearance. [ABSTRACT FROM AUTHOR]

Knowledge about complex spatial structures can be acquired through self-directed interaction with virtual models. In the present study, interactive controls enabled flexible exploration of desktop virtual multi-level building models from an allocentric perspective (providing zoom, rotation, and selection of building levels) as well as from the egocentric perspective (providing virtual movement). Short-time training for deliberate exploration were investigated with respect to spatial knowledge acquisition ( N = 115, 59 females and 56 males). Four training conditions were included: (1) no training, (2) interaction with a training model with a basic exploration task, (3) cognitive prompts stimulating organisation of spatial information, (4) cognitive and meta-cognitive prompts stimulating planning and controlling the exploration activity. In addition, spatial abilities, real-world spatial strategies and computer game experience were considered as aptitudes. Aptitude variables explained up to 30% of the variance in spatial learning and mediated an effect of sex. Training explained up to 10% of the variance in spatial learning. Qualified training with prompts (conditions 3, 4) did not improve spatial learning compared with training with the basic task (condition 2). Training strongly diminished the role of aptitudes. [ABSTRACT FROM AUTHOR]

Drawing on the concept of organisational behaviour, this research augments the concepts of social capital theory and organisational culture with one pioneering precursor and mediator, the sense of well-being, to develop an integrative understanding of the factors affecting individuals' knowledge-sharing behaviour within the more complex context of the virtual organisation of Taiwanese Non-governmental Organisations (NGOs). A field survey of 131 employees from the selected virtual organisation was analysed using Structural Equation Modeling (SEM) to examine the outcomes empirically. Our research offers a persuasive body of evidence supporting the notion that increasing employees' sense of well-being can successfully form a bridge that can connect social capital tendency, organisational culture and employees' knowledge-sharing behaviour. Surprisingly, and contrary to common belief, the integrated model shows that social capital tendency seems to play a more important role than organisational culture in affecting employees' sense of well-being within the virtual organisation in a Chinese cultural context. Consequently, this research reveals the subtle interplay of employees' sense of well-being, social capital tendency, organisational culture and knowledge-sharing behaviour, while the in-depth analysis provides strong support for knowledge management research and practice. [ABSTRACT FROM AUTHOR]