Special Needs Education, eLearning and the Reflective ePortfolio: Implications for Developing and Assessing Competence in Pre-Service Education.

This paper reports on the use of an eportfolio for assessing aspects of a Post-Graduate pre-service teacher education programme specifically in the context of special needs education in Northern Ireland. Participants were challenged to develop their individual eportfolios by selecting and presenting evidence for assessment drawn from diverse sources. The rationale for using eportfolios for assessment purposes was to offer students the opportunity to demonstrate competencies by documenting and reflecting upon academic and pedagogical learning during a one year Post Graduate Certificate of Education (PGCE) programme.

Awarding global grades in OSCEs: Evaluation of a novel eLearning resource for OSCE examiners

BACKGROUND:
A novel online resource has been developed to aid OSCE examiner training comprising a series of videos of OSCE performances that allow inter-examiner comparison of global grade decisions.

AIMS:
To evaluate this training resource in terms of usefulness and ability to improve examiner confidence in awarding global grades in OSCEs.

METHOD:
Data collected from the first 200 users included global grades awarded, willingness to change grades following peer comparison and confidence in awarding grades before and after training.

RESULTS:
Most (86.5%) agreed that the resource was useful in developing global grade scoring ability in OSCEs, with a significant improvement in confidence in awarding grades after using the training package (p<0.001).

CONCLUSIONS:
This is a useful and effective online training package. As an adjunct to traditional training it offers a practical solution to the problem of availability of examiners.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Creating an eLearning Resource to Improve Knowledge and Understanding of Pregnancy in the Context of HIV Infection

Patient narratives have much to teach healthcare professionals about the experience of living with a chronic condition. While the biomedical narrative of HIV treatment is hugely encouraging, the narrative of living with HIV continues to be overshadowed by a persuasive perception of stigma. This paper presents how we sought to translate the evidence from a qualitative study of the perspectives of HIV affected pregnant women and expectant fathers on the care they received, from the pre conception to post natal period, into educational material for maternity care practice. Narrative scripts were written based on the original research interviews, with care taken to reflect the key themes from the research. We explore the way in which the qualitative findings bring to life patient and partner experiences and what it means for nurses, midwives and doctors to be prepared to care for couples affected by HIV. In so doing, we challenge the inequity between the dominance of biomedical knowledge over understanding the patient experience in the preparation of health professionals to care for HIV affected women and men who are having a baby or seeking to have a baby.