Ana Stojanov

Research-based learning places strong emphasis on reflective thinking because of its role in developing research competence and supporting lifelong learning. However, the absence of a validated self-report scale makes it difficult for researchers and educators to assess research students' reflective thinking in a timely and cost-effective manner. To respond to this need, the present study developed the Reflective Thinking Inventory (RTI) for postgraduate research students. Using data from 454 participants, the RTI was empirically validated through cross-validation analyses. The final scale consisted of 14 items grouped into two factors: process reflection (evaluating what and how they conducted their research) and premise reflection (examining personal assumptions or contextual issues relevant to their research). The RTI demonstrated acceptable reliability, convergent and discriminant validity, and strong measurement invariance across participant subgroups. Furthermore, both of the RTI factors were positively associated with research competence. The validated RTI provides a practical tool for measuring reflective thinking among research students and for informing programme design and evaluation.

This chapter examines the dual role of ChatGPT in student learning, focusing on its potential to both enhance academic performance and pose risks to academic integrity. Through a series of studies involving university students, the chapter explores various uses of ChatGPT, such as brainstorming, idea generation, summarizing complex content, and providing feedback on assignments. While many students reported using the tool to support their learning and self-regulation, there were concerns regarding its potential for academic misconduct, particularly when students relied on it to write assignments or summarize texts for tests. The research suggests that academic misconduct is not necessarily an inherent consequence of using ChatGPT but rather depends on the extent to which students over-rely on the tool for completing tasks. The chapter highlights the importance of ethical use of AI in education, proposing that educational efforts should focus on guiding students to use ChatGPT as a supplementary learning aid rather than a substitute for deep engagement with academic material. Finally, it suggests that workshops and institutional policies can help students navigate the ethical boundaries of using AI tools, promoting responsible and critical use to enhance learning outcomes while maintaining academic integrity.

This study examined associations between perceived use of specific conflict styles and mental health outcomes during the COVID-19 pandemic in a nationally representative sample of Swiss participants. Specifically, this study investigated how conflict styles and conflict profiles related to anxiety, depression, and loneliness during the pandemic. Indirect fighting emerged as the most consistent predictor of the three negative mental health outcomes. The combination of high indirect fighting and high yielding was associated with anxiety, the combination of high indirect fighting and low collaborating with depression, and high indirect fighting alone with loneliness. Moreover, Direct Conflict Managers-individuals who reported high collaboration and low use of indirect conflict styles-tended to report lower levels of anxiety, depression, and loneliness than those with other conflict profiles. Building on existing health and interpersonal communication scholarship, these findings highlight the potential psychological risks associated with indirect conflict behaviors during crises such as the COVID-19 pandemic and suggest that collaborative approaches to conflict may be linked to better mental health outcomes.

Multiple instances of rebranding of corporations or sports teams, or changes of personal names suggest that imposed change of symbols that people identify with leads to resistance towards the symbol change. In this paper, we examine the predictive role of sacred values, identity fusion, identification and essentialism in explaining such resistance, in a unique political context of a national referendum to change Macedonia to North Macedonia. Participants (ethnic Macedonians, N = 301) took a survey measuring these variables, along with their voting intentions and behaviour, 1 week prior to a national referendum on the name change, and again several weeks later. The results indicated that while all variables predicted negative attitudes towards the name change, only considering the name a sacred value and, to a lesser extent, being identified with the country, uniquely predicted this outcome, and only sacred values uniquely predicted voting behaviour. A large proportion of the participants were fully fused with the name "Macedonia," which may have limited its predictive value. A cross-lagged analysis suggested a potential causal path from national identification to viewing the name as sacred, essentializing it and becoming fused with it. This study is the first to examine the psychological mechanisms that drive resistance to symbol change.

The dark triad, consisting of narcissism, Machiavellianism, and psychopathy, is a robust predictor of academic misconduct. In this study, we examine if communication about academic integrity topics moderates this relationship. US students (N = 319) recruited via the CloudResearch and Prolific platforms completed an online questionnaire measuring the dark triad traits, SACCIA (Sufficiency, Accuracy, Clarity, Contextualisation, Interpersonal Adaptation) communication, and academic misconduct. Overall, the results provided initial support for the hypothesis: the more SACCIA-conforming the communication was, the weaker was the link between the dark triad and academic misconduct, albeit the interaction term did not always reach statistical significance. The findings suggest a potential avenue for reducing socially undesirable behavior among individuals high on the dark triad.

Academic misconduct by students is a serious issue that threatens the public trust in higher education institutions. In the current study, we examine whether SACCIA (Sufficient, Accurate, Clear, Contextualised and Interpersonally Adaptive) communication predicts lower academic misconduct via attitudes towards cheating and understanding what 'counts' as academic misconduct. Participants (N = 319) completed an online questionnaire in Qualtrics measuring SACCIA-adherent communication, academic misconduct, positive attitudes towards cheating and understanding what constitutes misconduct, along with control variables (Big 5 personality traits and Honesty-Humility). The results indicated that the effect of SACCIA communication on academic misconduct was exerted via attitudes, but not via understanding of what constitutes 'academic misconduct'. The more SACCIA-conforming the communication was, the lower the positive attitudes towards cheating, and the lower the positive attitudes the less self-reported misconduct. The findings suggest that universities could facilitate negative attitudes towards cheating and subsequently lower misconduct if they communicate about academic integrity in a SACCIA-adherent manner.

Although ChatGPT, a state-of-the-art, large language model, seems to be a disruptive technology in higher education, it is unclear to what extent students rely on this tool for completing different tasks. To address this gap, we asked university students (N = 490) recruited via CloudResearch to rate the extent to which they rely on ChatGPT for completing 13 tasks identified in a previous pilot study. Five distinct profiles emerged: ‘Versatile low reliers’ (38.2%) were characterised by low overall self-reported reliance across the tasks, while ‘all-rounders’ (10.4%) had high overall self-reported reliance. The ‘knowledge seekers’ (16.5%) scored particularly high on tasks such as content acquisition, information retrieval and summarising of texts, while the ‘proactive learners’ (11.8%) on tasks such as obtaining feedback, planning and quizzing. Finally, the ‘assignment delegators’ (23.1%) relied on ChatGPT for drafting assignments, writing homework and having ChatGPT write their assignment for them. The findings provide a nuanced understanding of how students rely on ChatGPT for learning. •Latent profile analysis with 490 university students revealed five distinct reliance profiles on ChatGPT.•Nearly 40% of students were ‘versatile low reliers’, showing minimal reliance on ChatGPT across different tasks.•16.5% were “knowledge seekers” exhibiting reliance for content acquisition, information retrieval, and summarization.•11.8% of participants relied on ChatGPT for obtaining feedback, planning, and quizzing (“proactive learners”).•“Assignment Delegators” (23.1%) relied on ChatGPT for drafting assignments and writing homework.

This study investigates whether people's responses to official communications about COVID-19 could be "profiled" with respect to socio-economic-demographic and behavioral characteristics. Such profiles could enhance the effectiveness of future crisis management through the use of profile-adapted communications that maximize message comprehension. A representative web panel survey (742 respondents) was conducted across Switzerland in February 2022 to assess the population's reaction to COVID-19 communications during the pandemic. Latent profile analysis was conducted to explore if distinct profiles of reactions to the communications would emerge, and how each of them relate to conspiracy mentality and SED measures. The analyses revealed three latent profiles: "Compliant supporters" (54%), "defiant deniers" (23.6%), and "anxious skeptics" (22.4%). Respondents with high conspiracy mentality were more likely to belong to "defiant deniers" or "anxious skeptics." Each profile was characterized by distinct SED and behavioral features (discussed in the paper). The COVID-19 pandemic demonstrated that one communication does not work for all people. Our study evidenced three different types of respondent profiles that require profile-adapted communications for more effective crisis control. Our study is the first to profile people's responses to COVID-19 communications in a systematic, person-centered way. The results can be used for more effective future crisis management that delivers to each profile's communicative needs.

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.