It has been long argued that learning analytics has the potential to act as a “middle space” between the learning sciences and data analytics, creating technical possibilities for exploring the vast amount of data generated in online learning environments. One common learning analytics intervention is the learning dashboard, a support tool for teachers and learners alike that allows them to gain insight into the learning process. Although several related works have scrutinised the state-of-the-art in the field of learning dashboards, none have addressed the theoretical foundation that should inform the design of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our critical examination reveals the most common educational concepts and the context in which they have been applied. We find evidence that current designs foster competition between learners rather than knowledge mastery, offering misguided frames of reference for comparison.

Self-regulated learning (SRL) is a crucial higher-order skill required by learners of the 21st century, who will need to become lifelong learners to adapt to the continually changing environments. Literature provides examples of tools for scaffolding SRL in online environments. In this article, we provide the state-of-the-art concerning tools that support SRL in terms of theoretical models underpinning development, supported SRL processes, tool functionalities, used data and visualizations. We reviewed 42 articles published between 2008 and 2020, including information from 25 tools designed to support SRL. Our findings indicate that: 1) many of the studies do not explicitly specify the SRL theoretical model used to guide the design process of the tool; 2) goal setting, monitoring, and self-evaluation are the most prevalent SRL processes supported through functionalities, such as content navigation, user input forms, collaboration features, and recommendations; 3) the relationship between tool functionalities and SRL processes are rarely described; and 4) few tools assess the impact on learners’ SRL process and learning performance. Finally, we highlight some lessons learned that might contribute to implementing future tools that support learners’ SRL processes.

Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving PhD students as participants, each of them wearing a Fitbit HR wristband and having their application on their computer recorded during their learning and working activities throughout the day. A software infrastructure for collecting multimodal learning experiences was implemented. As part of this infrastructure a Data Processing Application was developed to pre-process, analyse and generate predictions to provide feedback to the users about their learning performance. Data from different sources were stored using the xAPI standard into a cloud-based Learning Record Store. The participants of the experiment were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as markers to train a Linear Mixed Effect Model to generate learner-specific predictions of the learning performance. We discuss the advantages and the limitations of the used approach, highlighting further development points.

This paper introduces a learning analytics policy and strategy framework developed by a cross-European research project team — SHEILA (Supporting Higher Education to Integrate Learning Analytics), based on interviews with 78 senior managers from 51 European higher education institutions across 16 countries. The framework was developed adapting the RAPID Outcome Mapping Approach (ROMA), which is designed to develop effective strategies and evidence-based policy in complex environments. This paper presents four case studies to illustrate the development process of the SHEILA framework and how it can be used iteratively to inform strategic planning and policy processes in real world environments, particularly for large-scale implementation in higher education contexts. To this end, the selected cases were analyzed at two stages, each a year apart, to investigate the progression of adoption approaches that were followed to solve existing challenges, and identify new challenges that could be addressed by following the SHEILA framework.