The management of academic workloads: full report on findings

The pressures on UK higher education (from explicit competition and growth in student numbers, to severe regulatory demands) are greater than ever, and have resulted in a steady increase in measures taken by universities to actively manage their finances and overall quality. These pressures are also likely to have impacted on staff and, indeed, recent large surveys in the sector have indicated that almost half of respondents find their workloads unmanageable. Against this background it would seem logical that the emphasis on institutional interventions to improve finance and quality, should be matched by similar attention given to the allocation of workloads to staff, and a focus on how best to utilise people’s time - the single biggest resource available within universities. Thus the aim of this piece of research was to focus on the processes and practices surrounding the allocation of staff workloads within higher education. Ten diverse organisations were selected for study: six universities in the UK, two overseas universities and two non higher education (but knowledge-intensive) organisations. In each, a crosssection of staff was selected, and in-depth interviews carried out. A total of 59 such interviews were carried out across the ten organisations. By identifying typical practices, as well as interesting alternatives, views on the various strengths and weaknesses of each of their workload allocation approaches was collated; and associated factors requiring attention identified. Through an extensive process of analysis, approaches which promoted more equitable loads for individuals, and which might provide synergies for institutions were also investigated

Navigating large-scale ‘‘desk-top’’ virtual buildings: effects of orientation aids and familiarity

Two experiments investigated components of participants’ spatial knowledge when they navigated large-scale ‘‘virtual buildings’’ using ‘‘desk-top’’ (i.e., nonimmersive) virtual environments (VEs). Experiment 1 showed that participants could estimate directions with reasonable accuracy when they traveled along paths that contained one or two turns (changes of direction), but participants’ estimates were significantly less accurate when the paths contained three turns. In Experiment 2 participants repeatedly navigated two more complex virtual buildings, one with and the other without a compass. The accuracy of participants’ route-finding and their direction and relative straight-line distance estimates improved with experience, but there were no significant differences between the two compass conditions. However, participants did develop significantly more accurate spatial knowledge as they became more familiar with navigating VEs in general

Evaluating the stability requirements for mounting and dismounting from the top of leaning ladders

This report details the methodology and findings of an investigation into the suitability of leaning ladders as a means to access high surfaces. This work has been funded by the Health and Safety Executive to provide a factual basis on which to make recommendations regarding safe practice. In particular it addresses a gap in the knowledge generated in previous studies into safe ladder use. This gap is generated by those individuals for whom the pressures of work make use of a ladder necessary but for whom safe practice is compromised. In particular, environmental demands, multiple unpredictable locations and challenging tasks combine to make a ladder an obvious, yet arguably unsafe, choice of equipment

Speaker Normalization Methods for Vowel Cognition: Comparative Analysis Using Neural Network and Nearest Neighbor Classifiers

Intrinsic and extrinsic speaker normalization methods are systematically compared using a neural network (fuzzy ARTMAP) and L1 and L2 K-Nearest Neighbor (K-NN) categorizers trained and tested on disjoint sets of speakers of the Peterson-Barney vowel database. Intrinsic methods include one nonscaled, four psychophysical scales (bark, bark with endcorrection, mel, ERB), and three log scales, each tested on four combinations of F0 , F1, F2, F3. Extrinsic methods include four speaker adaptation schemes, each combined with the 32 intrinsic methods: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). ARTMAP and KNN show similar trends, with K-NN performing better, but requiring about ten times as much memory. The optimal intrinsic normalization method is bark scale, or bark with endcorrection, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods is LT, CSi, LS, and CS, with fuzzy ARTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225