An application of multilevel modelling techniques to the longitudinal study of student progress in a modular degree course

This thesis presents a longitudinal study of undergraduate achievement within a\ud modular first degree course, analysing the academic records of a cohort of students who\ud graduated from the Modular Degree Programme at Oxford Brookes University. Multilevel\ud models are fitted to the marks achieved by members of this cohort in each module taken.\ud Level 1 units are individual module entries, nested within occasions within individual\ud student's programmes. These models were fitted by maximum likelihood and used to study\ud the effects of both student and module characteristics on performance. The effects of these\ud factors on mean marks, on the consistency of students performance and on the variation\ud between students were studied by including complex variation at level 1 and random effects\ud at student level in the models. In addition, individual progress charts were fitted, showing\ud how patterns of progress vary from one student to another.\ud Reviewing the hierarchical structure, it was found that a more complex, crossclassified\ud structure is needed to represent the data accurately. This recognises that individual\ud module entries are clustered within modules, as well as within students. Fitting large\ud multilevel cross-classified models is computationally difficult, however newly developed\ud MCMC estimation techniques allowed a model based on the more complex structure and\ud including random effects and complex variation to be fitted. This analysis shows how MCMC\ud estimation techniques can be used to fit a large cross-classified multilevel model,\ud incorporating random effects and complex variation. The results obtained describe students'\ud progress over the period of their degree course and measure the effects, other things being\ud equal, of factors such as assessment methods, age and subject on mean levels of achievement,\ud consistency of performance and the variation between students, providing a model for future\ud studies of achievement within a modular framework

Incidence of and risk factors for perineal trauma: A prospective observational study

Background: Our aim was to describe the range of perineal trauma in women with a singleton vaginal birth and estimate the effect of maternal and obstetric characteristics on the incidence of perineal tears. Methods: We conducted a prospective observational study on all women with a planned singleton vaginal delivery between May and September 2006 in one obstetric unit, three freestanding midwifery-led units and home settings in South East England. Data on maternal and obstetric characteristics were collected prospectively and analysed using univariable and multivariable logistic regression. The outcome measures were incidence of perineal trauma, type of perineal trauma and whether it was sutured or not. Results: The proportion of women with an intact perineum at delivery was 9.6% (125/1,302) in nulliparae, and 31.2% (453/1,452) in multiparae, with a higher incidence in the community (freestanding midwifery-led units and home settings). Multivariable analysis showed multiparity (OR 0.52; 95% CI: 0.30-0.90) was associated with reduced odds of obstetric anal sphincter injuries (OASIS), whilst forceps (OR 4.43; 95% CI: 2.02-9.71), longer duration of second stage of labour (OR 1.49; 95% CI: 1.13-1.98), and heavier birthweight (OR 1.001; 95% CI: 1.001-1.001), were associated with increased odds. Adjusted ORs for spontaneous perineal truama were: multiparity (OR 0.42; 95% CI: 0.32-0.56); hospital delivery (OR 1.48; 95% CI: 1.01-2.17); forceps delivery (OR 2.61; 95% CI: 1.22-5.56); longer duration of second stage labour (OR 1.45; 95% CI: 1.28-1.63); and heavier birthweight (OR 1.001; 95% CI: 1.000-1.001). Conclusions: This large prospective study found no evidence for an association between many factors related to midwifery practice such as use of a birthing pool, digital perineal stretching in the second stage, hands off delivery technique, or maternal birth position with incidence of OASIS or spontaneous perineal trauma. We also found a low overall incidence of OASIS, and fewer second degree tears were sutured in the community than in the hospital settings. This study confirms previous findings of overall high incidence of perineal trauma following vaginal delivery, and a strong association between forceps delivery and perineal trauma. © 2013 Smith et al; licensee BioMed Central Ltd

Institutions and the Diversity and Prevalence of Multinationals’ Knowledge-Augmenting Subsidiaries

Multinational corporations (MNCs) increasingly seek to gain access to, and exploit, locationally specific sources of advanced knowledge and technological capabilities, creating a need to explain (1) the diversity among these facilities and (2) how institutions influence MNCs’ abilities to invest in different subsidiary types. Extending debates on firms’ knowledge‐augmenting activities, the authors integrate institutions into their analytical framework to a greater extent than previous work has done. Moreover, existing contributions provide typologies of R&D subsidiaries. In contrast, the authors focus on a particular subset of subsidiaries, knowledge‐augmenting ones, and put forward a theory to explain their variety and their prevalence, enabling them to identify previously neglected subsidiary types that have important managerial and policy implications. By downplaying the diversity of these subsidiaries, existing work has not been able to capture the full range of managerial challenges as well as the costs and benefits of different subsidiary types to host countries. The authors, therefore, problematize firms’ abilities to gain access to foreign knowledge‐generating assets, highlight the importance of institutional environments, provide policy recommendations and identify areas for future research

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

An assignment based on data driven journalism: a case study

In a module designed to develop skills in presenting and evaluating statistics, students of mathematics and statistics were given an assignment asking them to research and write a piece of data driven journalism. Data driven journalism is a new phenomenon which has expanded rapidly due to the growth in open data, new visualisation tools and online reporting in newspapers, periodicals and blogs. The assignment provided students with a writing assignment that was individual, small-scale, research-based and embedded within their discipline. The students were asked to formulate a research question that could be investigated using survey data available from an electronic data archive. The result of the investigation was to be written up as a piece of data driven journalism for online publication, including a data visualisation. In addition to using discipline-based skills and written communication, the assignment required students to use research skills and digital literacy. An assignment set in the context of writing for the public extends students’ writing experience beyond the domains of discipline-based professional reports and academic writing. Data driven journalism provides opportunities to develop students’ writing alongside other skills for employment and can be used to design assessments for a wide range of disciplines.</jats:p