Reflective writing is an important educational practice to train reflective thinking. Currently, researchers must manually analyze these writings, limiting practice and research because the analysis is time and resource consuming. This study evaluates whether machine learning can be used to automate this manual analysis. The study investigates eight categories that are often used in models to assess reflective writing, and the evaluation is based on 76 student essays (5080 sentences) that are largely from third-and second-year health, business, and engineering students. To test the automated analysis of reflection in writings, machine learning models were built based on a random sample of 80% of the sentences. These models were then tested on the remaining 20% of the sentences. Overall, the standardized evaluation shows that five out of eight categories can be detected automatically with substantial or almost perfect reliability, while the other three categories can be detected with moderate reliability (Cohen's κ ranges between .53 and .85). The accuracies of the automated analysis were on average 10% lower than the accuracies of the manual analysis. These findings enable reflection analytics that is immediate and scalable.

Yttrium silicates are promising materials for improved oxidation and erosion protection for carbon fiber‐reinforced composites. A two‐layer coating system of low‐pressure plasma‐sprayed yttrium silicate on chemical vapor deposition‐SiC‐precoated C/C–SiC was tested under atmospheric re‐entry conditions simulated within a plasma wind tunnel test facility. The thermal expansion behavior of Y2SiO5 and Y2Si2O7 was investigated. The chemical compatibility with and without increasing oxygen partial pressure at the interface of the two‐layer system was calculated by the CALPHAD method. The calculations were compared with experimental results. Furthermore, a thermodynamic explanation is presented to understand and predict the observed coating failure mechanism, identified as blister formation.

By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced R.L.L. Sie et al.learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided.

Most distance learning institutions collect vast amounts of learner and learning data. Making sense of this "Big Data" can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, in 2012 the Open University UK (OU) instigated a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical, and sense-making advice to staff and senior management. Given substantial changes within the OU over the last 18 months (e.g., new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case-study provides an in-depth review of lessons learned of 5 years of data wrangling. Using semistructured interviews with key stakeholders (10 senior managers/associate deans) and ten Data Wranglers (DWs), a clear mismatch was identified in terms of resources, expertise, and skills that can effectively address key needs from Faculties. Furthermore, inconsistencies in terms of reporting and responding to bespoke requests were noted by stakeholders. Given the essential role of DW for the OU, a new DW structure is proposed to ensure effective provision of in-depth, evidence-based data analyses, pedagogical insight, and actionable advice for Faculties. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.

Accessibility cannot be fully achieved through adherence to technical guidelines, and must include processes that take account of the diverse contexts and needs of individuals. A complex yet important aspect of this is to understand and utilise feedback from disabled users of systems and services. Open comment feedback can complement other practices in providing rich data from user perspectives, but this presents challenges for analysis at scale. In this paper, we analyse a large dataset of open comment feedback from disabled students on their online and distance learning experience, and we explore opportunities and challenges in the analysis of this data. This includes the automated and manual analysis of content and themes, and the integration of information about the respondent alongside their feedback. Our analysis suggests that procedural themes, such as changes to the individual over time, and their experiences of interpersonal interactions, provide key examples of areas where feedback can lead to insight for the improvement of accessibility. Reflecting on this analysis in the context of our institution, we provide recommendations on the analysis of feedback data, and how feedback can be better embedded into organisational processes. CCS Concepts• User Characteristics~People with Disabilities • Accessibility ~Accessibility design and evaluation methods.