LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates…

Play is considered an important aspect of Early Childhood Education and Care. However, the relationship between play and learning is often taken for granted both in research and praxis. In this article, we study our own research group's empirical work over a 40-year period, and how we have used the concepts of play and learning. We observed that…

Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and…

This collection focuses on the role of higher education institutions concerning datafication as a complex phenomenon. It explores how the universities can develop data literac(ies) shaping tomorrow skills and "formae mentis" to face the most deleterious effects of datafication, but also to engage in creative and constructive ways with…

In massive open online courses (MOOCs), learners can interact with each other using discussion boards. Automatically inferring the states or needs of learners from their posts is of interest to instructors, who are faced with a high attrition in MOOCs. Machine learning has previously been successfully used to identify states such as confusion or…

Video has become a widespread tool for capturing naturalistic behavioral data. While mixed methods show great potential in understanding the active nature of children's interaction, only a few studies have developed mixed methods for video-based interaction research. This paper presents a mixed methods embodied interaction model appropriate for…

Analyzing qualitative data from learning processes is considered "messy" and time consuming (Chi in J Learn Sci 6(3):271-315, 1997). It is often challenging to summarize and synthesize such data in a manner that conveys the richness and complexity of learning processes in a clear and concise manner. Moreover, qualitative data often…

Purpose: Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this…

Why we need better reports of teacher professional development interventions for finding out what works where: Worldwide, billions of dollars are invested yearly in the professional development of in-service teachers ($14 billion in the USA alone, as of 2014; Bill & Melinda Gates Foundation, 2014). This makes the question of what effects TPD…

Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…

This paper investigates the intersection of big data and philosophy of education by considering big data's potential for addressing learning via a holistic process of coming-to-know. Learning, in this sense, cannot be reduced to the difference between a pre- and post-test, for example, as it is constituted at least as much by qualities of…

This systematic review is built on the seminal work by the New London Group in 1996. Few endeavours have synthesized findings of empirical studies pertaining to the effects and challenges of multiliteracies practices in various schooling and geographical contexts. Through a five-point Likert scale and a deductive and inductive thematic analysis,…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

The importance of temporality in learning has been long established, but it is only recently that serious attention has begun to be paid to the precise identification, measurement, and analysis of the temporal features of learning. From 2009 to 2016, a series of temporality workshops explored temporal concepts and data types, analysis methods for…

Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to develop control over their learning, and subsequently supporting more effective learning. However,…