It is commonly suggested that emerging technologies will revolutionize education. In this paper, two such emerging technologies, artificial intelligence (AI) and educational robots (ER), are in focus. The aim of the paper is to explore how teachers, researchers and pedagogical developers critically imagine and reflect upon how AI and robots could be used in education. The empirical data were collected from discussion groups that were part of a symposium. For both AI and ERs, the need for more knowledge about these technologies, how they could preferably be used, and how the emergence of these technologies might affect the role of the teacher and the relationship between teachers and students, were outlined. Many participants saw more potential to use AI for individualization as compared with ERs. However, there were also more concerns, such as ethical issues and economic interests, when discussing AI. While the researchers/developers to a greater extent imagined ideal future technology-rich educational practices, the practitioners were more focused on imaginaries grounded in current practice.
Understanding students' privacy concerns is an essential first step toward effective privacy‐enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non‐self‐disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non‐self‐disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non‐self‐disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non‐self‐disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy‐enhancing practices' development in higher education is offered. What is already known about this topic Addressing students' privacy is critical for large‐scale learning analytics (LA) implementation. Understanding students' privacy concerns is an essential first step to developing effective privacy‐enhancing practices in LA. Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA. What this paper adds The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education. It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self‐disclosure behaviour in the LA context of higher education. It offers a set of relevant implications for LA researchers and practitioners. Implications for practice and/or policy Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information. Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success. Contextual factors that may influence students' privacy concerns should be considered.
<div><strong>Purpose:</strong> The purpose of this paper is to present best practices and areas of improvement in Technical Communication (TC) analyzed with Lean values as a base. The purpose is also to analyze the results from a holistic perspective using the Synergy-4 model, a multi-perspective approach which considers four different spheres of an organization at a time in order to discover synergies.</div><div> </div><div><strong>Methodology/Approach:</strong> To fulfill the purpose, 15 interviews in four different companies were conducted. These were then analyzed and the results were categorized into a number of predefined Lean areas. The results from the Lean values were then further analyzed with the Synergy-4 model as a base. </div><div> </div><div><strong>Findings:</strong> Taking a Lean perspective could enhance the status of TC with regard to finding ways to incorporate the customer’s voice more clearly when it comes to strengthening the role of TC. The result from the analyses indicates that Lean and Synergy-4 can enrich each other.</div>
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