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Ignorance of the crowd:dysfunctional thinking in social networks
Get PDFCognitive dysfunction, and the resulting social behaviours, contribute to major social problems, ranging from polarisation to the spread of conspiracy theories. Most previous studies have explored these problems at a specific scale: individual, group, or societal. This study develops a synthesis that links models of cognitive failures at these three scales. First, cognitive limits and innate drives can lead to dysfunctional cognition in individuals. Second, cognitive biases and social effects further influence group behaviour. Third, social networks cause cascading effects that increase the intensity and scale of dysfunctional group behaviour. Advances in communications and information technology, especially the Internet and AI, have exacerbated established problems by accelerating the spread of false beliefs and false interpretations on an unprecedented scale, and have become an enabler for emergent effects hitherto only seen on a smaller scale. Finally, this study explores mechanisms used to manipulate people's beliefs by exploiting these biases and behaviours, notably gaslighting, propaganda, fake news, and promotion of conspiracy theories.</p
Human resource retention strategies to reduce swim instructor turnover intention
No full textResearch question: Leisure organisations’ most valued staff are often the most likely to leave. Leisure employees (e.g. swim instructors) are resources essential to organisational performance requiring proactive strategies by managers and human resource departments to retain talented individuals. The association between job satisfaction and turnover intention is well supported in the literature. The study aimed to identify and explain the current turnover intention of Victorian, Australia swim instructors. Methods: Using a modified version of Roodt’s Turnover Intention Theory, this study utilised semi-structured interviews with current swim instructors (n = 42; 62% female, 38% male). A deductive content analysis assisted to explain the turnover intention of current swim instructors. Results: Younger swim instructors (i.e. 18–28 years old) presented a high turnover intention. Older swim instructors (i.e. 45+ years old) presented a low turnover intention. Implications: Managers of swim schools should contemplate implementing retention strategies to reduce the turnover intention of younger swim instructors and consider the benefits of having a workforce containing older swim instructors. Supporting the industry to manage turnover intention might support in improving ineffective pedagogy and teaching, enable more individuals to participate in swimming lessons, and improve drowning outcomes.</p
Forms of agency enacted by international Ph.D. holders in Australia and Ph.D. returnees in China to negotiate employability
No full textThe notion of agency has been widely used in various disciplines but is relatively new in the field of employability of international graduates and returnees. This discussion paper addresses this gap by unpacking the determinants that influence the enactment of agency of international Ph.D. graduates in Australia and Ph.D. returnees in China. In essence, the key determinants in Australia include permanent residency, high expectations and discrimination from some local employers, and the development of multiple identities of international Ph.D. graduates. In contrast, the signifiers in China are Ph.D. returnees’ residency status, work experience, and the social networks that they establish with the authorities in organisations. To navigate these determinants, international Ph.D. graduates in Australia often enact three forms of agency: essential needs-response agency, structure-navigating agency, and strengths-based agency, whereas Ph.D. returnees in China are engaged with two forms of agency including extreme structure-navigating agency and social capital-based agency.</p
Creating equity for ethnically, linguistically, and religiously diverse students in school settings in the Myanmar public schools
No full textIn Myanmar, large diverse indigenous ethnicities exist, and, as a result, public schools consist of a multicultural and multilingual student population. Despite this, the education system proffers and embeds Myanmar’s dominant ideologies relating to culture, language and religion within all aspects of schooling. Students from minority backgrounds often struggle to gain legitimacy and build capital in a system that does not acknowledge diversity. Drawing upon Bourdieu’s concepts of social reproduction, field and capital, this study examines how multiculturalism and multilingualism are positioned within Myanmar’s education policies and how Myanmar’s school leaders and teachers reflect and respond to the needs of students from minority backgrounds within the complex political and educational setting. This qualitative case study captured the perspectives of five participants: two school leaders and three teachers. The findings reveal that students from minority backgrounds experience religious-based inequalities, cultural exclusion, and indifference towards their language backgrounds in public school settings
Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport
No full textIncentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.</p
Designing relational feedback:a rapid review and qualitative synthesis
No full textFeedback has long been considered a significant lever to enhance learning experience and success in higher education. However, students have shown much discontent with the current feedback practice. Feedback experienced as a relational process in which students feel recognized and valued is perceived as paramount for helping support the uptake of feedback and promote positive learning dispositions. However, little attention has been paid to suggesting how instructors in higher educational institutions can facilitate relational feedback. In response, we conducted a rapid literature review with a particular focus on the creation of feedback content. In total, 17 peer-reviewed publications on relational feedback are included. Based on a qualitative analysis of these papers, we developed a framework of 12 characteristics for offering relational feedback. These 12 characteristics were further categorised into four types of feedback conductive to a relational process, including (i) clarifying performance, (ii) suggesting improvement, (iii) inviting further communication, and (iv) evoking positive emotions. We examined how each of these characteristics supports the relational aspects of feedback. Based on the qualitative analysis results, we offered practical recommendations for instructors to apply relational feedback in their pedagogical practices.</p
How educational chatbots support self-regulated learning? A systematic review of the literature
No full textEngagement in self-regulated learning (SRL) may improve academic achievements and support development of lifelong learning skills. Despite its educational potential, many students find SRL challenging. Educational chatbots have a potential to scaffold or externally regulate SRL processes by interacting with students in an adaptive way. However, to our knowledge, researchers have yet to learn whether and how educational chatbots developed so far have (1) promoted learning processes pertaining to SRL and (2) improved student learning performance in different tasks. To contribute this new knowledge to the field, we conducted a systematic literature review of the studies on educational chatbots that can be linked to processes of SRL. In doing so, we followed the PRISMA guidelines. We collected and reviewed publications published between 2012 and 2023, and identified 27 publications for analysis. We found that educational chatbots so far have mainly supported learners to identify learning resources, enact appropriate learning strategies, and metacognitively monitor their studying. Limited guidance has been provided to students to set learning goals, create learning plans, reflect on their prior studying, and adapt to their future studying. Most of the chatbots in the reviewed corpus of studies appeared to promote productive SRL processes and boost learning performance of students across different domains, confirming the potential of this technology to support SRL. However, in some studies the chatbot interventions showed non-significant and mixed effects. In this paper, we also discuss the findings and provide recommendations for future research.</p
CARLA:Self-supervised contrastive representation learning for time series anomaly detection
No full textOne main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. The normal boundary is often defined tightly, resulting in slight deviations being classified as anomalies, consequently leading to a high false positive rate and a limited ability to generalise normal patterns. To address this, we introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series Anomaly detection (CARLA). While existing contrastive learning methods assume that augmented time series windows are positive samples and temporally distant windows are negative samples, we argue that these assumptions are limited as augmentation of time series can transform them to negative samples, and a temporally distant window can represent a positive sample. Existing approaches to contrastive learning for time series have directly copied methods developed for image analysis. We argue that these methods do not transfer well. Instead, our contrastive approach leverages existing generic knowledge about time series anomalies and injects various types of anomalies as negative samples. Therefore, CARLA not only learns normal behaviour but also learns deviations indicating anomalies. It creates similar representations for temporally close windows and distinct ones for anomalies. Additionally, it leverages the information about representations’ neighbours through a self-supervised approach to classify windows based on their nearest/furthest neighbours to further enhance the performance of anomaly detection. In extensive tests on seven major real-world TSAD datasets, CARLA shows superior performance (F1 and AU-PR) over state-of-the-art self-supervised, semi-supervised, and unsupervised TSAD methods for univariate time series and multivariate time series. Our research highlights the immense potential of contrastive representation learning in advancing the TSAD field, thus paving the way for novel applications and in-depth exploration.</p
A meta-analysis of knowledge hiding behavior in organizations:antecedents, consequences, and boundary conditions
No full textIn this meta-analysis study, we propose a comprehensive framework for investigating the factors related to knowledge-hiding (KH) behavior, and the boundary conditions in those different relationships. These factors include human resource (HR) practices, leadership, and personality traits that lead to KH behavior, as well as two types of consequences: psychological and behavioral outcomes, and performance-related outcomes. With 267 independent samples from 248 primary studies, we conducted meta-analytic correlations, relative weight analysis, meta-regression analyses, and meta-subgroup analyses. Results indicate significant relationships between HR practices, leadership styles, personality traits and KH. KH was found to significantly impact psychological and behavioral outcomes, as well as performance-related outcomes. We further examined the moderating roles of demographic and contextual factors on KH and its antecedents, as well as methodological factors on the relationship between KH and its consequences. We discussed the implications and future directions.</p
Performance analysis of a blockchain-based messaging system implementation for air cargo supply chains
No full textThe air cargo supply chain’s goal is to achieve fast shipment movement through multistage coordination among multiple air cargo stakeholders. The lack of transparency in the end-to-end supply chain and complex digital connectivity is what limits the traditional peer-to-peer communication flow in the fragmented air cargo industry. Blockchain technology can enhance communication transparency by streamlining multistage message flows via smart contracts and by acting as an intermediary to simplify digital connections among air cargo stakeholders. Despite the benefits of applying blockchain for air cargo messaging, as blockchain is an emerging technology, technical concerns in the areas of performance, privacy, and interoperability can hinder the practical adoption of the blockchain system. To address practical implementation concerns, a blockchain-based messaging system using the Algorand public blockchain is developed in our work to demonstrate the practicality of blockchain. Transaction data privacy can be protected by the encryption key exchange algorithm, which supports forward secrecy. Our experimental results provide practical performance insights into the Algorand blockchain for streamlined messaging from various aspects related to transaction submission, confirmation, and retrieval. Our results show that by leveraging the Algorand blockchain, our proposed system can offer scalable, efficient, reliable, and cost-effective communication channels for air cargo messaging.</p