Feedback on feedback

Northumbria University hosts a Centre for Excellence in Teaching and Learning (CETL) which specialises in the ‘Assessment for Learning’ Agenda (AfL). This agenda developed in response to the diverse needs and competencies of Northumbria’s learners. But are the issues addressed by AfL solely a concern in Northumbria? What challenges and possible solutions might other Higher Education institutions encounter or offer? This paper addresses such questions, by identifying, analysing, and reflecting upon an issue in student learning and support, relevant to the discipline of English Literature in another Higher Education teaching context: the attitudes of students and staff to feedback in the School of English, Queen’s University Belfast (2007). To do so, it references national statistical data, and general and subject-specific educational research and literature. As such, this paper offers 'feedback on feedback', exploring dialogue between teachers and learners

Senses of Unending in the Work of Sir John Davies

Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 dofinansowane zostało ze środków MNiSW w ramach działalności upowszechniającej nauk

“Some falls are means the happier to arise”: Processes of Jeopardy in Shakespeare’s Late Play

Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 dofinansowane zostało ze środków MNiSW w ramach działalności upowszechniającej nauk

Penalized estimation in large-scale generalized linear array models

Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package \verb+glamlasso+. It combines several ideas -- previously considered separately -- to obtain sparse estimates while at the same time efficiently exploiting the GLAM structure. In this paper the convergence of the algorithm is treated and the performance of its implementation is investigated and compared to that of \verb+glmnet+ on simulated as well as real data. It is shown that the computation time fo

Assessing Marlowe in Context

This essay outlines the design and rationale of an innovative assessment used on a module dedicated to Marlowe in a UK university