Making mole-hills out of mountains:Peer Mentoring in Engineering education - A case-study

Based on the emergent findings of a pilot study which examined the issues around introducing Peer Mentoring into an Engineering School, this paper, which is very much a 'work in progress', describes and discusses results from the first year of what will be a three year exploratory study. Focusing on three distinctive concepts integral to the student experience, Relationships, Variety and Synergy, the study follows an Action Research Design in that it aims to find a realistic and workable solution to issues of attrition within the Engineering School in which the Project and Study are set. Starting with the research question "Does Peer Mentoring improve engineering students' transition into university?"', the Pilot Project and Study will run for three years, each year building on the lessons of the previous year.

Investigating the role of parent and child characteristics in healthy eating intervention outcomes

While numerous studies have investigated the efficacy of interventions at increasing children's vegetable consumption, little research has examined the effect of individual characteristics on intervention outcomes. In previous research, interventions consisting of modelling and rewards have been shown to increase children's vegetable intake, but differences were identified in terms of how much children respond to such interventions. With this in mind, the current study investigated the role of parental feeding practices, child temperament, and child eating behaviours as predictors of intervention success. Parents (N = 90) of children aged 2-4 years were recruited from toddler groups across Leicestershire, UK. Parents completed measures of feeding practices, child eating behaviours and child temperament, before participating in one of four conditions of a home-based, parent led 14 day intervention aimed at increasing their child's consumption of a disliked vegetable. Correlations and logistic regressions were performed to investigate the role of these factors in predicting intervention success. Parental feeding practices were not significantly associated with intervention success. However, child sociability and food fussiness significantly predicted intervention success, producing a regression model which could predict intervention success in 61% of cases. These findings suggest that future interventions could benefit from being tailored according to child temperament. Furthermore, interventions for children high in food fussiness may be better targeted at reducing fussiness in addition to increasing vegetable consumption.

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan.