Investigating students’ perceptions of graduate learning outcomes in mathematics

The purpose of this study is to explore the perceptions mathematics students have of the knowledge and skills they develop throughout their programme of study. It addresses current concerns about the employability of mathematics graduates by contributing much needed insight into how degree programmes are developing broader learning outcomes for students majoring in mathematics. Specifically, the study asked students who were close to completing amathematics major (n=144) to indicate the extent to which opportunities to develop mathematical knowledge along with more transferable skills (communication to experts and non-experts, writing, working in teams and thinking ethically) were included and assessed in their major. Their perceptions were compared to the importance they assign to each of these outcomes, their own assessment of improvement during the programme and their confidence in applying these outcomes. Overall, the findings reveal a pattern of high levels of students'agreement that these outcomes are important, but evidence a startling gap when compared to students' perceptions of the extent to which many of these - communication, writing, teamwork and ethical thinking - are actually included and assessed in the curriculum, and their confidence in using such learning

A cyclone climatology of the British-Irish Isles 1871-2012

The British-Irish Isles (BI) lie beneath the North Atlantic storm track year-round and thus are impacted by the passage of extra-tropical cyclones. Given recent extreme storminess and projections of enhanced winter cyclone activity for this region, there is much interest in assessing the extent to which the cyclone climate of the region may be changing. We address this by assessing a 142-year (1871-2012) record of cyclone frequency, intensity and 'storminess' derived from the 20th Century Reanalysis V2 (20CR) dataset. We also use this long-term record to examine associations between cyclone activity and regional hydroclimate. Our results confirm the importance of cyclone frequency in driving seasonal precipitation totals which we find to be greatest during summer months. Cyclone frequency and storminess are characterized by pronounced interannual and multi-decadal variability which are strongly coupled to atmospheric blocking in the Euro-Atlantic region, but we detect no evidence of an increasing trend. We observe an upward trend in cyclone intensity for the BI region, which is strongest in winter and consistent with model projections, but promote caution interpreting this given the changing data quality in the 20CR over time. Nonetheless, we assert that long-term reconstruction is helpful for contextualizing recent storminess and for identifying emerging changes in regional hydroclimate linked to cyclones

An evaluation of persistent meteorological drought using a homogeneous Island of Ireland precipitation network

This paper investigates the spatial and temporal properties of persistent meteorological droughts using the homogeneous Island of Ireland Precipitation (IIP) network. Relative to a 1961–1990 baseline period it is shown that the longest observed run of below average precipitation since the 1850s lasted up to 5 years (10 half-year seasons) at sites in southeast and east Ireland, or 3 years across the network as a whole. Dry spell and wet spell length distributions were represented by a first-order Markov model which yields realistic runs of below average rainfall for individual sites and IIP series. This model shows that there is relatively high likelihood (p = 0.125) of a 5-year dry spell at Dublin, and that near unbroken dry runs of 10 years or more are conceivable. We suggest that the IIP network and attendant rainfall deficit modelling provide credible data for stress testing water supply and drought plans under extreme conditions

The stumbling blocks of integrating quantitative skills in science

The Science Higher Education community has acknowledged the essential role of quantitative skills (QS) as a graduate learning outcome. However, efforts to build QS across science degree programs have been meet with a range of obstacles that are inhibiting the development of QS to an appropriate standard. This presentation, drawing on interview data from the ALTC funded QS in Science project which used a case study approach, details the challenges institutions have found in trying to ensure that QS are developed and embraced in science curricula. Interview data (n = 48) from academic staff involved in the case studies revealed several broad categories that significantly impacted on embedding QS effectively in the science curriculum: 1) the attitude and background of students undertaking science courses, 2) the constraints of the various science degree program structures

The perspectives of scientists and mathematicians on quantitative skills

Mathematics is important in science, and becoming increasingly so. Not surprisingly, the scientific community is calling for graduates with higher standards of quantitative skills (QS), that is, the ability to apply mathematical and statistical thinking and reasoning in the context of science. How are academics addressing this QS challenge? Some see this as an interdisciplinary endeavour, with science and mathematics academics working together to develop the QS of students in undergraduate science programs. We present evidence which suggests that scientists and mathematicians have different attitudes to what is happening in universities currently. This work is a part of the ALTC funded QS in Science project in which 48 interviews were conducted with academics in both teaching and leadership roles from 11 universities in Australia and two in the USA

Multi-century trends to wetter winters and drier summers in the England and Wales precipitation series explained by observational and sampling bias in early records

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid-latitude atmospheric circulation, a predictor in long-term European gridded precipitation data sets, the assessment of drought and extremes, tree-ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi-centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality-assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre-1870 winters are likely biased low due to gauge under-catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre-1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi-centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re-appraisal of established long-term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long-term European precipitation series

The 'dirty dozen' of freshwater science: detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected, or erroneously archived data introduce uncertainty regarding the magnitude, rate, and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/measurement procedures, postprocessing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation, and comparison between sites, outlier, and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation, or urbanization); modifications to instrumentation or inconsistent observer behavior; mismatched or misrepresentative sampling in space and time; treatment of missing values, postprocessing and data storage errors. Also for raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data

The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data