This special issue deals with three areas. Learning design is the practice of devising effective learning experiences aimed at achieving defined educational objectives in a given context. Teacher inquiry is an approach to professional development and capacity building in education in which teachers study their own and their peers' practice. Learning analytics use data about learners and their contexts to understand and optimise learning and the environments in which it takes place. Typically, these three-design, inquiry and analytics-are seen as separate areas of practice and research. In this issue, we show that the three can work together to form a virtuous circle. Within this circle, learning analytics offers a powerful set of tools for teacher inquiry, feeding back into improved learning design. Learning design provides a semantic structure for analytics, whereas teacher inquiry defines meaningful questions to analyse.

Previous research shows that digital game‐based learning (DGBL) can have positive effects on engagement, motivation and learning, and that using narratives may reinforce these effects. A systematic review identified 15 DGBL systems that report effects from their use of narratives. A gap in the field, however, is the lack of a common model to categorize and isolate narratives in DGBL to enable an analysis and comparison of how, and under what conditions, narratives have effects on learning in DGBL systems. The ludo narrative variable model (LNVM) that has been used to isolate and categorize narratives in research on commercial video games is a candidate to fill this gap. This research has investigated the potential of this model for DGBL and resulted in an extended LNVM (eLNVM) that can be used to isolate and categorize narratives in DGBL. The 15 DGBL systems were categorized on the eLNVM and the results show that there are characteristics of DGBL systems with positive self‐reported effects that separate them from other DGBL systems. Furthermore, it was possible to identify characteristics of the narrative modeling that are associated with positive effects on engagement, motivation and learning. The paper concludes with a description of how the eLNVM will be used in future research.

Research on instructional and learning design is ‘booming’ in Europe, although there has been a move from a focus on content and the way to present it in a formal educational context (i.e., instruction), to a focus on complex learning, learning environments including the workplace, and access to learner data available in these environments. We even see the term ‘learning experience design’ (Neelen and Kirschner 2020) to describe the field. Furthermore, there is an effort to empower teachers (and even students) as designers of learning (including environments and new pedagogies), and to support their reflection on their own practice as part of their professional development (Hansen and Wasson 2016; Luckin et al. 2016; Wasson et al. 2016). While instructional design is an often heard term in the United States and refers to “translating principles of learning and instruction into plans for instructional materials, activities, information resources, and evaluation” (Smith and Ragan 1999), Europe tends to lean more towards learning design as the key for providing efficient, effective, and enjoyable learning experiences. This is not a switch from an instructivist to a constructivist view nor from a teacher-centred to a student-centred paradigm. It is, rather, a different mind-set where the emphasis is on the goal (i.e., learning) rather than the approach (i.e., instruction). Designing learning opportunities in a technology enhanced world builds on theories of human learning and cognition, opportunities provided by technology, and principles of instructional design. New technology both expands and challenges some instructional design principles by opening up new opportunities for distance collaboration, intelligent tutoring and support, seamless and ubiquitous learning and assessment technologies, and tools for thinking and thought. In this article, the authors give an account of their own and other research related to instructional and learning design, highlight related European research, and point to future research directions.

Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature.

Researchers have recently been calling for new models of teacher education and professional development for the 21st century. Teacher inquiry, where the teacher's own practice is under investigation, can be seen both as a way to improve day-to-day teaching in the classroom and as professional development for teachers. As such, it should also have a role in teacher education. In this article, we present the iterative development of the TISL Heart, a theory-practice model and method of teacher inquiry into student learning, which has a particular emphasis on the use of student results generated in the information and technology-rich classroom. This article proposes that this practice-near model is particularly relevant for teacher education, as it draws upon existing practices in using student data at a progressive school that focuses on the use of technology to enhance student learning. The article concludes by discussing the implications for its role in teacher education, particularly related to data literacy and its use in teaching.