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Abstract:In this paper we examine emerging ways to describe and structure learning material, learning tasks and learning situations. In particular, we consider three different approaches, looking at common issues and differences in emphasis. The approaches are: learning patterns [1], inspired by the architectural patterns of Alexander [2]; learning design, as described in the IMS Learning Design specification [3], which itself draws on Educational Modelling Language developed at the Open University of the Netherlands; and, learning activities as used in the Learning Activity Management System [4].Keywords: learning design; patterns; e-learning; learning activities; learning technology standards; reuse Biographical notes: Patrick McAndrew is a senior lecturer in the Institute of Educational Technology at The Open University where he teaches and researches in the use of technology in support of learning. His work examines ways to design for active engagement by learners working together. This has involved studies in task based approaches to learning and their representation as learning designs within knowledge sharing environments. In 2001 he cofounded the UserLab research team which works within the Computers and Learning research group to undertake projects in e-learning.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

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