Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level

© 2019 Australasian Journal of Educational Technology. Despite the recent surge of interest in learning analytics (LA), their adoption in everyday classroom practice is still slow. Knowledge gaps and lack of inter-stakeholder communication (particularly with educational practitioners) have been posited as critical factors for previous LA adoption failures. Yet, what issues should researchers, practitioners and other actors communicate about, when considering the adoption of an LA innovation in a particular context? We reviewed and synthesised existing literature on four focus areas related to LA, their adoption, implications for practice, and more general factors that have emerged as crucial when studying everyday classroom adoption of technologies (i.e., classroom orchestration). This synthesis resulted in two conversational frameworks and an inter-stakeholder communication tool. These can be used to guide and support conversations and decision-making about the adoption of LA innovations. We illustrate their usefulness with examples of use in ongoing LA adoption processes in Australia, Spain and Estonia

Current and future multimodal learning analytics data challenges

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, highfrequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic

Integrating orchestration of ubiquitous and pervasive learning environments

Ubiquitous and pervasive computing devices, such as interactive tabletops, whiteboards, tablets and phones, have the potential to enhance the management and awareness of learning activities in important ways. They provide students with natural ways to interact with collaborators, and can help teachers create and manage learning tasks that can be carried out both in the classroom and at a distance. But how can these emerging technologies be successfully integrated into current teaching practice? This paper proposes an approach to integrate, from the technological perspective, collaborative learning activities using these kinds of devices. Our approach is based on the concept of orchestration, which tackles the critical task for teachers to coordinate student's learning activities within the constraints of authentic educational settings. Our studies within authentic learning settings enabled us to identify three main elements that are important for ubiquitous and pervasive learning settings. These are i) regulation mechanisms, ii) interconnection with existing web-based learning environments, and iii) awareness tools

Lesson observation data in learning analytics datasets: Observata

Observational data can be used to illuminate different areas of teaching and learning process and enrich Learning Analytics data. Majority of lesson observation tools provide observational data that is not compliant with LA datasets. The paper presents Observata \u2013 a tablet computer application for context-aware semantic annotations of significant events during real time lesson observations. During the demo-session we expect the participants to engage in the discussion and provide feedback on the prototype

Learning Analytics for Learning Design: Towards Evidence-Driven Decisions to Enhance Learning

As the fields of learning analytics and learning design mature, the convergence and synergies between them become an important area for research. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing learning. Hence, this paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. The search was performed in seven academic databases, resulting in 38 papers included in the main analysis. The review demonstrates ongoing design patterns and learning phenomena that improve learning, by providing more comprehensive background of the current landscape of learning analytics for learning design and its impact on the current status of learning technologies. Consequently, future research should consider how to capture and systematize learning design data. Moreover, it should evaluate and document what learning design choices made by educators using what learning analytics techniques influence learning experiences and learning performances over time

Learning Analytics Research: Using Meta-Review to Inform Meta-Synthesis Authors

Research in learning analytics is proliferating as scholars continue to find better and more engaging ways to consider how data can help inform evidence-based decisions for learning and learning environments. With well over a thousand articles published in journals and conferences with respect to learning analytics, only a handful or articles exist that attempt to synthesize the research. Further, a meta-review of those articles reveals a lack of consistency in the scope of included studies, the confluence of educational data mining activities and “big data” as a parameter for inclusion, and the reporting of actual strategies and analytic methods used by the included studies. To fill these gaps within existing reviews of learning analytics research, this metasynthesis follows procedures outlined by Cooper to reveal developments of learning analytics research. The results include a number of metrics showing trends and types of learning analytic studies through 2017 that include which fields are publishing and to what extent, what methods and strategies are employed by these studies, and what domains remain largely yet unexplored