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Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders' expectations of LA across Higher Education Institutions (HEIs) for large‐scale implementations that take their needs into account. Objectives: This study aims to contribute to knowledge about individual LA expectations of European higher education students. It may facilitate the strategy of stakeholder buy‐in, the transfer of LA insights across HEIs, and the development of international best practices and guidelines. Methods: To this end, the study employs a 'Student Expectations of Learning Analytics Questionnaire' (SELAQ) survey of 417 students at the Goethe University Frankfurt (Germany) Based on this data, Multiple Linear Regressions are applied to determine how these students position themselves compared to students from Madrid (Spain), Edinburgh (United Kingdom) and the Netherlands, where SELAQ had already been implemented at HEIs. Results and Conclusions: The results show that students' expectations at Goethe University Frankfurt are rather homogeneous regarding 'LA Ethics and Privacy' and 'LA Service Features'. Furthermore, we found that European students generally show a consistent pattern of expectations of LA with a high degree of similarity across the HEIs examined. European HEIs face challenges more similar than anticipated. The HEI experience with implementing LA can be more easily transferred to other HEIs, suggesting standardized LA rather than tailor‐made solutions designed from scratch. Lay Description: What is currently known about this topic: Implementing Learning Analytics is a promising but challenging task.SELAQ is a questionnaire that can be used to determine students' expectations of Learning Analytics.Several institutions in Europe applied SELAQ to implement Learning Analytics that meet the needs of their students. What this paper adds to this: This study applies SELAQ for the first time at a German university and compares the results to studies from the UK, Spain, and the Netherlands.The participating students at the German university show relatively homogeneous expectations of Learning Analytics.The expectations towards Learning Analytics of students across Europe are comparable. The implications of study findings for practitioners: Learning Analytics systems should be shared across institutions rather than being implemented separately as tailored solutions.More standardized or modular Learning Analytics systems are reasonable. [ABSTRACT FROM AUTHOR]

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations. Lay Description: What is already known about this topic: Many MLA systems are designed without learners' involvement.Understanding of how multimodal data can support learners' authentic needs is needed.MLA have the potential to enable the automated generation of models to provide real‐time feedback. What this paper adds: Understanding how learners perceive multimodal data and the learning context.The findings outline the soft challenges in using multimodal data in the learning practice.Ten dimensions were identified to serve as guidelines for researchers and designers.The paper reports learners' perspectives describing new, intriguing, and under‐developed ideas about potential uses of multimodal data in educational context.The discussion on the need for human‐centred design approaches for educational technologies has been presented. Implications for practice and/or policy: Relevant pointers for what is appropriate and ethically necessary when designing learning technologies.Change in perspective among educators, on how they address progress and engagement in learning. [ABSTRACT FROM AUTHOR]