Metrics of student dissatisfaction and disagreement: longitudinal explorations of a national survey instrument

This study explores dissatisfaction and neutrality metrics from 12 years of a national-level undergraduate student survey. The notion of dissatisfaction is much less prevalent in the narratives surrounding student survey outcomes, and the underpinning metrics are seldom considered. This is despite an increasingly vociferous debate about ‘value for money’ of higher education and the positioning of students as consumers in a marketised sector. We used machine learning methods to explore over 2.7 million national survey outcomes from 154 institutions to describe year-on-year stability in the survey items that best predicted dissatisfaction and neutrality, together with their similarity to known metric predictors of satisfaction. The widely publicised annual increases in student ‘satisfaction’ are shown to be the result of complex reductions in the proportions of disagreement and neutrality across different survey dimensions. Due to the widespread use of survey metrics in university league tables, we create an anonymised, illustrative table to demonstrate how UK institutional rankings would have differed if dissatisfaction metrics had been the preferred focus for reporting. We conclude by debating the tensions of balancing the provision of valuable information about dissatisfaction, with perpetuating negative impacts that derive from this important subset of the survey population

Reducing the energy footprint of cellular networks with delay-tolerant users

International audienceOne of the most promising techniques to drastically reduce the energy consumption of cellular networks is the use of sleep-mode (SM) methods: when the traffic load is low, some components of the network, such as a base station (BS), can be switched off. In this case, the traffic load is managed by BSs that stay on. In this paper, we investigate how user cooperation can further reduce the energy consumption of a cellular network that uses SM strategies. In particular, we study how sleeping periods can be extended when users tolerate a delay before the start of their service. We propose two delay-tolerant-user-aware SM strategies and provide mathematical grounds for the evaluation of their performance. We evaluate the strategies in the context of LTE networks with realistic daily traffic patterns. The results show considerable daily energy reductions (up to 21% compared to the always- on paradigm and up to 15% compared to the SM strategy without user cooperation