Content and discontent: a qualitative exploration of obstacles to elearning engagement in medical students

BACKGROUND: Elearning is ubiquitous in healthcare professions education. Its equivalence to 'traditional' educational delivery methods is well established. There is a research imperative to clarify when and how to use elearning most effectively to mitigate the potential of it becoming merely a 'disruptive technology.' Research has begun to broadly identify challenges encountered by elearning users. In this study, we explore in depth the perceived obstacles to elearning engagement amongst medical students. Sensitising concepts of achievement emotions and the cognitive demands of multi-tasking highlight why students' deeply emotional responses to elearning may be so important in their learning.

METHODS: This study used focus groups as a data collection tool. A purposeful sample of 31 participated. Iterative data gathering and analysis phases employed a constant comparative approach to generate themes firmly grounded in participant experience.

RESULTS: Key themes that emerged from the data included a sense of injustice, passivity and a feeling of being 'lost at sea'. The actual content of the elearning resource provided important context.

CONCLUSIONS: The identified themes have strong emotional foundations. These responses, interpreted through the lens of achievement emotions, have not previously been described. Appreciation of their importance is of benefit to educators involved in curriculum development or delivery.

Integration of cad/ tool path data for 5-axis step-nc machining of free form / irrregular contoured surfaces

This research paper presents the work on feature recognition, tool path data generation and integration with STEP-NC (AP-238 format) for features having Free form / Irregular Contoured Surface(s) (FICS). Initially, the FICS features are modelled / imported in UG CAD package and a closeness index is generated. This is done by comparing the FICS features with basic B-Splines / Bezier curves / surfaces. Then blending functions are caculated by adopting convolution theorem. Based on the blending functions, contour offsett tool paths are generated and simulated for 5 axis milling environment. Finally, the tool path (CL) data is integrated with STEP-NC (AP-238) format. The tool path algorithm and STEP- NC data is tested with various industrial parts through an automated UFUNC plugin.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.