Using mirror and other internal surveys in order to improve student experience

This article is the first stage of a project which considers how best to use the data collected from mirror surveys and other internal student surveys to enhance the student experience, with a subsidiary aim of thereby enhancing National Student Survey (NSS) scores. The second stage, which is underway at present, combines the theoretical basis and debate explored in this article with detailed statistical analysis of internal and external survey results, to provide a greater evidential basis for decision-making and strategic planning. The research was supported as a 2011-12 Learning Development Project, at City University London, and is intended to inform educational discussion and strategy. The interim findings discussed below are readily transferable to other disciplines and other universities. Universities have put a great deal of effort into improving student satisfaction, but not always with measurable results. Throughout the existence of the NSS, universities have experienced significant variance between student satisfaction as represented by internal measures and the levels of satisfaction reported in the NSS. This has been the case even when the internal measures take the form of mirror surveys, i.e. surveys which mirror or closely resemble the questions on the current version of the NSS. Although general morale factors and events beyond a university’s control may play a strong role in the scores, they do not necessarily explain the differences, especially where the internal questions are based on those from the NSS. Both measures may be an accurate representation of student satisfaction but measuring subtly different factors, or other influences may be operating. By examining this issue, this project aims to enable better planning for the future and the development of appropriate, tailored responses to issues. The interim findings reflect examples of best practice and next steps for the strategic use of such data, including free-text comments

Meaningful Categorisation of Novice Programmer Errors

The frequency of different kinds of error made by students learning to write computer programs has long been of interest to researchers and educators. In the past, various studies investigated this topic, usually by recording and analysing compiler error messages, and producing tables of relative frequencies of specific errors diagnostics produced by the compiler. In this paper, we improve on such prior studies by investigating actual logical errors in student code, as opposed to diagnostic messages produced by the compiler. The actual errors reported here are more precise, more detailed and more accurate than the diagnostic produced automatically

Co-Teaching for Unsupervised Domain Adaptation and Expansion

Unsupervised Domain Adaptation (UDA) is known to trade a model's performance on a source domain for improving its performance on a target domain. To resolve the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to adapt the model for the target domain as UDA does, and in the meantime maintain its performance on the source domain. For both UDA and UDE, a model tailored to a given domain, let it be the source or the target domain, is assumed to well handle samples from the given domain. We question the assumption by reporting the existence of cross-domain visual ambiguity: Due to the lack of a crystally clear boundary between the two domains, samples from one domain can be visually close to the other domain. We exploit this finding and accordingly propose in this paper Co-Teaching (CT) that consists of knowledge distillation based CT (kdCT) and mixup based CT (miCT). Specifically, kdCT transfers knowledge from a leader-teacher network and an assistant-teacher network to a student network, so the cross-domain visual ambiguity will be better handled by the student. Meanwhile, miCT further enhances the generalization ability of the student. Comprehensive experiments on two image-classification benchmarks and two driving-scene-segmentation benchmarks justify the viability of the proposed method

Strategies for promoting active learning in tutorials: Insights gained from a first-year accounting subject

This paper provides a description of the experience of, and reflection on, employing authentic learning and teaching activities to encourage participation and active learning in tutorial classes in a first-year accounting subject. The lack of student participation and engagement in tutorials is recognised as an issue by many academics. Student’s interest in developing accounting knowledge is further dampened by a perceived lack of relevance between textbook theories and practice. Using an action research model, this paper therefore describes and analyses strategies for dealing with these problems and stimulating student interest in learning