Understanding Accessibility as a Process through the Analysis of Feedback from Disabled Students

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

Scholarly insight Autumn 2017:a Data wrangler perspective

As the OU is going through several fundamental changes, it is important that strategic decisions made by Faculties and senior management are informed by evidence-based research and insights. One way how Data Wranglers provide insights of longitudinal development and performance of OU modules is the Key Metric Report 2017. A particular new element is that data can now also be unpacked and visualised on a Nation-level. As evidenced by the Nation-level reporting, there are substantial variations of success across the four Nations, and we hope that our interactive dashboards allow OU staff to unpack the underlying data. The second way Data Wranglers provide insight to Faculties and Units is through the Scholarly insight report series. Building on the previous two reports whereby we reported on substantial variation and inconsistencies in learning designs and assessment practices within qualifications across the OU, in this Scholarly insight Autumn 2017 report we address four big pedagogical questions that were framed and co-constructed together with the Faculties and LTI units. Many Faculties and colleagues have reacted positively on our Scholarly insight Spring 2017 report, whereby for the first time we were able to show empirically that students experienced substantial variations in success within 12 large OU qualifications. As evidenced in our previous report, 55% of variation in students’ success over time was explained by OU institutional factors (i.e., how students were assessed within their respective module; how students were able to effectively transition from one learning design of one module to the next one), rather than students’ characteristics, engagement and behaviour. We have received several queries and questions from Faculties and Units about how to better understand these students’ journeys, and how qualifications and module designs could be better aligned within their respective qualification(s). As these are complex conceptual and Big Pedagogy questions, in Chapter 1 we continued these complex analyses by looking at the transitional processes of the first two modules that OU students take, and how well aligned these modules and qualification paths are. In Chapter 2, we explored the more fine-grained, qualitative, and lived experiences of 19 students across a range of qualifications to understand how OU grading practices and (in)consistencies of assessment and feedback influenced their affect, behaviour, and cognition. In addition to building on previous topics, we introduced two new Scholarly insights in Chapter 3 and Chapter 4. As the OU is increasingly using learning analytics to support our staff and students, in Chapter 3 we analysed the impact of giving Predictive Learning Analytics to over 500 Associate Lecturers across 31 modules on student retention. Finally, in Chapter 4 we explored the impact of first presentations of new modules on pass rates and satisfaction, whereby we were able to bust another myth that may have profound implications for Student First Transformation. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight

Scholarly insight Spring 2018: a Data wrangler perspective

In the movie classic Back to the Future a young Michael J. Fox is able to explore the past by a time machine developed by the slightly bizarre but exquisite Dr Brown. Unexpectedly by some small intervention the course of history was changed a bit along Fox’s adventures. In this fourth Scholarly Insight Report we have explored two innovative approaches to learn from OU data of the past, which hopefully in the future will make a large difference in how we support our students and design and implement our teaching and learning practices. In Chapter 1, we provide an in-depth analysis of 50 thousands comments expressed by students through the Student Experience on a Module (SEAM) questionnaire. By analysing over 2.5 million words using big data approaches, our Scholarly insights indicate that not all student voices are heard. Furthermore, our big data analysis indicate useful potential insights to explore how student voices change over time, and for which particular modules emergent themes might arise. In Chapter 2 we provide our second innovative approach of a proof-of-concept of qualification path way using graph approaches. By exploring existing data of one qualification (i.e., Psychology), we show that students make a range of pathway choices during their qualification, some of which are more successful than others. As highlighted in our previous Scholarly Insight Reports, getting data from a qualification perspective within the OU is a difficult and challenging process, and the proof-of-concept provided in Chapter 2 might provide a way forward to better understand and support the complex choices our students make. In Chapter 3, we provide a slightly more practically-oriented and perhaps down to earth approach focussing on the lessons-learned with Analytics4Action. Over the last four years nearly a hundred modules have worked with more active use of data and insights into module presentation to support their students. In Chapter 3 several good-practices are described by the LTI/TEL learning design team, as well as three innovative case-studies which we hope will inspire you to try something new as well. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties, we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight. We hope that you see some potential in the two innovative approaches, and perhaps you might want to try some new ideas in your module. While a time machine has not really been invented yet, with the increasing rich and fine-grained data about our students and our learning practices we are getting closer to understand what really drives our students

Scholarly insight Winter 2019: a Data wrangler perspective

Henry Ford famously said that “Any customer can have a car painted any colour that he wants so long as it is black.” Similarly, our Prime Minister Theresa May indicated in 2016 to aim for a “red, white and blue Brexit”. While the Open University (OU) has been open for 50 years to all learners, we are aware that our students have unique and different learning needs, experiences, and expertise. The OU recognises that we need to carefully listen to our students, and focus on their needs. Nonetheless, in some of our narratives we tend to simplify and generalise these multiple, complex student voices into one common voice. As highlighted in all three chapters in this fifth Scholarly Insight report, working intensively together with the Faculties our Data wranglers have found strong empirical evidence that our students indeed have very unique and distinct voices, which influence their engagement, behaviour, and study success. In Chapter 1 we worked closely together with the four Faculties to further unpack the qualitative feedback and students’ comments of the Student experience on a module (SEAM) survey (e.g., do Open degree students have different narratives when providing feedback; do high performing students “talk” differently from low performing students). Indeed our text analytics toolkit has highlighted that Open degree students speak differently from others (e.g., needing enough study time). Furthermore, higher achieving students report on different topics (e.g., content, feedback, group) than lower achievers (e.g., help, problem, experience). The OU needs to carefully balance these different voices, as addressing one concern from a high achieving student might not necessarily benefit other students, and vice-versa. In Chapter 2 describes three approaches of students selecting different module pathways towards qualification completion. For one Open Degree programme in Creative Writing we find that 268 unique paths are taken by students, whereby some paths are more successful than others. Follow-up analyses in QUAL2F3 indicate substantial differences in pass rates and success depending on the respective route, specialism, and pathways students are taking. Sign-posting these “successful” paths to OU staff and students may help students to make more informed decisions of what to study next. Finally in Chapter 3 we explore how students make timing decisions when to study for a module, and how so-called study break and assessment preparation weeks could help to provide more flexibility for our students. Study breaks are weeks during which no learning activities are planned or take place, and students are not expected to study for a module. Our big data analyses with 123,916 students and 205 OU modules indicate that the way OU designs study weeks has a substantial impact in how students study over time. Study break weeks substantially increase the chances of students to pass a module, while assessment preparation weeks are not related to pass rates We hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our understanding of the different student voices and qualification pathways

P900: A Putative Novel ERP Component that Indexes Counter-Measure Use in the P300-Based Concealed Information Test

Countermeasures pose a serious threat to the effectiveness of the Concealed Information Test (CIT). In a CIT experiment, Rosenfeld and Labkovsky in Psychophysiology 47(6):1002–1010, (2010) observed a previously unknown positive ERP component at about 900 ms poststimulus at Fz and Cz that could potentially serve as an index of countermeasure use. Here, we explored the hypothesis that this component, termed P900, occurs in response to a signal that no further specific response is required in a trial, and could thus appear in countermeasure users that respond differentially depending on the stimulus that appears. In the present experiments, subjects viewed four non-meaningful (irrelevant) dates and one oddball date. In three experiments, we examined P900’s antecedent conditions. In the first, the unique item was a personally relevant oddball (the subject’s birthdate). In a second, the unique item was a non-personally relevant oddball (an irrelevant date in a unique font color). In a third, all dates were irrelevant. We speculated that the presence of an oddball would not be necessary for P900. All participants made countermeasure-like responses following two specific irrelevant dates. As hypothesized, P900s were seen to non-responded-to irrelevant and oddball stimuli in all subjects but not to responded-to irrelevant stimuli, and the presence of an oddball was not necessary for elicitation of P900. This finding has potential application in deception settings—the presence of a P300 accompanied by the presence of a P900 in response to non-countered stimuli could provide evidence of incriminating knowledge accompanied by the attempt to use countermeasures to evade detection

Scholarly insight 2016: a Data wrangler perspective

We are pleased to offer you our first Scholarly insight 2016: a Data wrangler perspective. The OU is going through several fundamental changes, whereby strategic, pedagogical informed research and insight what drives student learning and academic performance is essential. Making sense of Big Data in particular can be a challenge, especially when data is stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. In 2012 the Open University UK (OU) instigated a Data Wrangling initiative, which provided every Faculty with a dedicated academic with expertise in 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, increased reliance on analytics), an extensive discussion with various stakeholders within the Faculties was initiated to make sure that data wranglers provide effective pedagogical insight based upon best practice and evidence-based analyses and research (see new Data wrangler structure). Demand for actionable insights to help support OU staff and senior management in particular with module and qualification design is currently strong (Miller & Mork, 2013), especially a desire for evidence of impact of “what works” (Ferguson, Brasher, et al., 2016). Learning analytics are now increasingly taken into consideration when designing, writing and revising modules, and in the evaluation of specific teaching approaches and technologies (Rienties, Boroowa, et al., 2016). A range of data interrogation and visualization tools developed by the OU supports this (Calvert, 2014; Toetenel & Rienties, 2016b). With the new ways of working with Data Wrangling, first we have provided our basic statistical analyses in form of our Key Metrics report. Second, from January 2017 onwards we will focus again on dealing with bespoke requests from Faculties, and where possible share the insights across all Schools and Faculties. Third, this Scholarly insight has a different purpose to previous Data wrangler work, namely we aim to provide state-of-the-art and forward looking insights into what drives our students and staff in terms of learning and learning success. Based upon consultation with the Faculties, seven key cross-Faculty themes were identified that influence our students’ learning experiences, academic performance, and retention. The first five chapters focus on how the OU designs modules, formative and summative assessments and feedback, helps students from informal to formal learning, and how these learning designs influence student satisfaction. All five chapters indicate that the way we design our modules fundamentally influences student satisfaction, and perhaps more importantly academic retention. Clear guidelines and good-reads are provided for how module teams, ALs, and others can improve our focus on Students First. In Chapter 6-7, we specifically address how individual student demographics (e.g., age, ethnicity, prior education) and accessibility in particular influence the students’ learning journeys, with concrete suggestions how to support our diverse groups of students. Note that each chapter can be read independently and in any particular order. We are looking forward to your feedback

Scholarly insight Spring 2017: A Data Wrangler Perspective

We are pleased to offer you our Scholarly insight Spring 2017: a Data wrangler perspective. The OU is going through several fundamental changes, whereby strategic, pedagogical informed research and insight what drives student learning and academic performance is essential. One of the strategic priorities of Students First (Open University UK, 2017b) and big shifts in OU Redesign (Open University UK, 2017a) is to “simplify and clarify choice for students based upon recommend learning and/or appropriate directed pathways”. Making sense of Big Data in particular can be a challenge, especially students are following different pathways and qualification routes across the OU. Demand for actionable insights to help students make the best out of their OU experience and qualification in particular is currently strong (Open University UK, 2017a, 2017b), especially a desire for evidence of impact of “what works” (Ferguson & Clow, 2017). Furthermore, insights from big data and learning analytics in particular are now increasingly taken into consideration at the OU when designing, writing and revising modules, and in the evaluation of specific teaching approaches and technologies (Herodotou, Heiser, & Rienties, 2017; Herodotou, Rienties, et al., 2017; Rienties, Boroowa, et al., 2016). With the new ways of working with Data Wrangling, first we have provided our basic statistical analyses in form of our Key Metrics report. Based upon three well-attended workshops to further improve our working with the Faculties in January/February 2017, for which we are forever grateful, we have further fine-tuned our ways of working, and Key Metrics report in particular (e.g., adding nations data). Second, the Datawrangler team has worked extensively with the Faculties on “bespoke requests” from Faculties, and where possible shared the insights across all Schools and Faculties. Some of this work is reflected in this report, while other insights will be shared in the next Autumn report. Third, the focus groups indicated that overall the Scholarly insight report was well received, but was rather lengthy and lacked specific input and recommendations for each Faculty. Therefore, we have worked with key stakeholders to identify the top 10 big data and pedagogical “concerns and problems” in each Faculty, which we afterwards narrowed down to a top 5, and subsequently a top 3. Working organically in various Faculty sub-group meetings and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly Insights can help to inform and help first and foremost our students, but also spark some ideas how to further improve our module designs and qualification pathways. Some of the topics in this top 10 will be addressed in the next Autumn report, and we are of course keen to hear what other topics require Big Data and Scholarly insight

P900: A Putative Novel ERP Component that Indexes Countermeasure Use in the P300-Based Concealed Information Test

Abstract Countermeasures pose a serious threat to the effectiveness of the Concealed Information Test (CIT). In a CIT experiment, Rosenfeld and Labkovsky in Psychophysiology 47(6):1002-1010, (2010) observed a previously unknown positive ERP component at about 900 ms poststimulus at Fz and Cz that could potentially serve as an index of countermeasure use. Here, we explored the hypothesis that this component, termed P900, occurs in response to a signal that no further specific response is required in a trial, and could thus appear in countermeasure users that respond differentially depending on the stimulus that appears. In the present experiments, subjects viewed four non-meaningful (irrelevant) dates and one oddball date. In three experiments, we examined P900's antecedent conditions. In the first, the unique item was a personally relevant oddball (the subject's birthdate). In a second, the unique item was a non-personally relevant oddball (an irrelevant date in a unique font color). In a third, all dates were irrelevant. We speculated that the presence of an oddball would not be necessary for P900. All participants made countermeasure-like responses following two specific irrelevant dates. As hypothesized, P900s were seen to non-responded-to irrelevant and oddball stimuli in all subjects but not to responded-to irrelevant stimuli, and the presence of an oddball was not necessary for elicitation of P900. This finding has potential application in deception settings-the presence of a P300 accompanied by the presence of a P900 in response to non-countered stimuli could provide evidence of incriminating knowledge accompanied by the attempt to use countermeasures to evade detection

Magnetic resonance microimaging of the spinal cord in the SOD1 mouse model of amyotrophic lateral sclerosis detects motor nerve root degeneration

Amyotrophic lateral sclerosis (ALS) is characterized by selective degeneration of motor neurons. Current imaging studies have concentrated on areas of the brain and spinal cord that contain mixed populations of sensory and motor neurons. In this study, ex vivo magnetic resonance microimaging (MRM) was used to separate motor and sensory components by visualizing individual dorsal and ventral roots in fixed spinal cords. MRM at 15 pm in plane resolution enabled the axons of pure populations of sensory and motor neurons to be measured in the lumbar region of the SOD1 mouse model of ALS. MRM signal intensity increased by 38.3% (p < 0.05) exclusively in the ventral motor nerve roots of the lumbar spinal cord of ALS-affected SOD1 mice compared to wildtype littermates. The hyperintensity was therefore limited to white matter tracts arising from the motor neurons, whereas sensory white matter fibers were unchanged. Significant decreases in ventral nerve root volume were also detected in the SOD1 mice, which correlated with the axonal degeneration observed by microscopy. These results demonstrate the usefulness of MRM in visualizing the ultrastructure of the mouse spinal cord. The detailed 3D anatomy allowed the processes of pure populations of sensory and motor neurons to be compared. (C) 2011 Elsevier Inc. All rights reserved

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570