The validity of critical thinking tests for predicting degree performance: A longitudinal study

This study explored the validity of using critical thinking tests to predict final psychology degree marks over and above that already predicted by traditional admission exams (A-levels). Participants were a longitudinal sample of 109 psychology students from a university in the United Kingdom. The outcome measures were: total degree marks; and end of year marks. The predictor measures were: university admission exam results (A-levels); critical thinking test scores (skills & dispositions); and non-verbal intelligence scores. Hierarchical regressions showed A-levels significantly predicted 10% of the final degree score and the 11-item measure of ‘Inference skills’ from the California Critical Thinking Skills Test significantly predicted an additional 6% of degree outcome variance. The findings from this study should inform decisions about the precise measurement constructs included in aptitude tests used in the higher education admission process.

Empathy in UK pharmacy students: assessing differences by gender, level in the degree programme, part-time employment and medical status

Background: Empathy is an important aspect of patient–healthcare professional interactions.Aims: To investigate whether gender, level in the degree programme, employment and health status affected empathy scores of undergraduate pharmacy students.Method: All undergraduate pharmacy students (n=529) at Queen’s University Belfast were invited via email to completean online validated empathy questionnaire. Empathy scores were calculated and non-parametric tests used to determine associations between factors.Results: Response rate was 60.1% (318/529) and the mean empathy score was 106.19. Scores can range from 20 to 140,with higher scores representing a greater degree of empathy. There was no significant difference between genders (p=0.211). There was a significant difference in scores across the four levels of the programme (p<0.001); scores were lowest at Level 1 and greatest at Level 4 (final year). There were no significant differences in scores for respondents who had a part-time job, a chronic condition, or took regular medication in comparison to those who did not (p=0.028,p=0.880, p=0.456, respectively).Conclusion: A reasonable level of empathy was found relative to other studies; this could be further enhanced at lower levels of the degree pathway.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

A Low Complexity Detector with MRC Diversity Reception for MCIK-OFDM

Multi-carrier index keying (MCIK) is a recently developed transmission technique that exploits the sub-carrier indices as an additional degree of freedom for data transmission. This paper investigates the performance of a low complexity detection scheme with diversity reception for MCIK with orthogonal frequency division multiplexing (OFDM). For the performance evaluation, an exact and an approximate closed form expression for the pairwise error probability (PEP) of a greedy detector (GD) with maximal ratio combining (MRC) is derived. The presented results show that the performance of the GD is significantly improved when MRC diversity is employed. The proposed hybrid scheme is found to outperform maximum likelihood (ML) detection with a substantial reduction on the associated computational complexity.

A spatial approach to network generation for three properties:Degree distribution, clustering coefficient and degree assortativity

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values. © Copyright JASSS.

Measuring the shape of degree distributions

Degree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focusses on degree, the distribution of other node based network properties could also be described with Gini coefficients.