This study employed a mixed-methods approach to identify subgroups among Finnish employees based on the interplay between meta-work—such as troubleshooting—and technostress, the strain caused by technology use. Using data from an online questionnaire, we identified four technostress subgroups through thematic, correspondence, and cluster analyses: (1) moderate technostress and intense meta-work engagement, (2) high technostress and moderate meta-work engagement, (3) low technostress with effective coping and low meta-work engagement, and (4) low technostress with ineffective coping and very low meta-work engagement. Each subgroup varied by technological skills, age, and organizational factors. Meta-work was found to increase burdens and worsen technostress. To minimize technostress, organizations should address meta-work effects, offer tailored training, implement user-friendly technologies, and create supportive work environments. By automating and simplifying processes, the workload of employees can be reduced. Understanding technostress and meta-work improves well-being and productivity, so studying their long-term effects and coping strategies is important.

The pervasiveness of technical systems in our lives calls for a broad understanding of the interaction between humans and technology. Affinity for technology interaction (ATI) scale measures the tendency of a person to actively engage or to avoid interaction with technological systems, including both software and physical devices. This research presents a psychometric analysis of a Finnish version of the ATI scale. The data consisted of 796 responses of students in a Finnish university. The data were analyzed utilizing factor analysis and both nonparametric and parametric item response theory. The Finnish version of the ATI scale proved to be essentially unidimensional, showing high reliability estimates, and forming a strong Mokken scale. Hierarchical multiple regression analysis showed that men had a slightly higher affinity for technology than women when controlling for age and field of study; however the effect size was small.

This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge.

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher's support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.