Learning to read with a critical eye: cultivating discerning readers of media reports with a science component.

It is important for young people to be able to read science-related media reports with discernment. ‘Getting Newswise’ was a research project designed to enable science and English teachers, working collaboratively, to equip pupils through the curriculum with critical reading skills appropriate for science news. Phase one of the study found that science and English teachers respond differently to science news articles and eight categories of critical response were identified. These findings informed phase two, in which classroom activities were devised whereby pupils examined, evaluated and responded to science-related news reports. Science-English collaboration had positive outcomes for pupil understanding

Learning to manage complexity through simulation: students' challenges and possible strategies

Many have called for medical students to learn how to manage complexity in healthcare. This study examines the nuances of students' challenges in coping with a complex simulation learning activity, using concepts from complexity theory, and suggests strategies to help them better understand and manage complexity.Wearing video glasses, participants took part in a simulation ward-based exercise that incorporated characteristics of complexity. Video footage was used to elicit interviews, which were transcribed. Using complexity theory as a theoretical lens, an iterative approach was taken to identify the challenges that participants faced and possible coping strategies using both interview transcripts and video footage.Students' challenges in coping with clinical complexity included being: a) unprepared for 'diving in', b) caught in an escalating system, c) captured by the patient, and d) unable to assert boundaries of acceptable practice.Many characteristics of complexity can be recreated in a ward-based simulation learning activity, affording learners an embodied and immersive experience of these complexity challenges. Possible strategies for managing complexity themes include: a) taking time to size up the system, b) attuning to what emerges, c) reducing complexity, d) boundary practices, and e) working with uncertainty. This study signals pedagogical opportunities for recognizing and dealing with complexity.

Evaluation of creative problem solving abilities in undergraduate structural engineers through interdisciplinary problem based learning

For a structural engineer, effective communication and interaction with architects cannot be underestimated as a key skill to success throughout their professional career. Structural engineers and architects have to share a common language and understanding of each other in order to achieve the most desirable architectural and structural designs. This interaction and engagement develops during their professional career but needs to be nurtured during their undergraduate studies. The objective of this paper is to present the strategies employed to engage higher order thinking in structural engineering students in order to help them solve complex problem-based learning (PBL) design scenarios presented by architecture students. The strategies employed were applied in the experimental setting of an undergraduate module in structural engineering at Queen’s University Belfast in the UK. The strategies employed were active learning to engage with content knowledge, the use of physical conceptual structural models to reinforce key concepts and finally, reinforcing the need for hand sketching of ideas to promote higher order problem-solving. The strategies employed were evaluated through student survey, student feedback and module facilitator (this author) reflection. The strategies were qualitatively perceived by the tutor and quantitatively evaluated by students in a cross-sectional study to help interaction with the architecture students, aid interdisciplinary learning and help students creatively solve problems (through higher order thinking). The students clearly enjoyed this module and in particular interacting with structural engineering tutors and students from another discipline

Phonic skills mediate improved literacy outcomes in at-risk primary school children: results from the Ready-to-Learn Programme

Background
Learning to read is a key goal during primary school: reading difficulties may curtail children’s learning trajectories. Controversy remains regarding what types of interventions are effective for children at risk for academic failure, such as children in disadvantaged areas. We present data from a complex intervention to test the hypothesis that phonic skills and word recognition abilities are a pivotal and specific causal mechanism for the development of reading skills in children at risk for poorer literacy outcomes.
Method
Over 500 pupils across 16 primary schools took part in a Cluster Randomised Controlled Trial from school year 1 to year 3. Schools were randomly allocated to the intervention or the control arm. The intervention involved a literacy-rich after-school programme. Children attending schools in the control arm of the study received the curriculum normally provided. Children in both arms completed batteries of language, phonic skills, and reading tests every year. We used multilevel mediation models to investigate mediating processes between intervention and outcomes.
Findings
Children who took part in the intervention displayed improvements in reading skills compared to those in the control arm. Results indicated a significant indirect effect of the intervention via phonics encoding.
Discussion
The results suggest that the intervention was effective in improving reading abilities of children at risk, and this effect was mediated by improving children’s phonic skills. This has relevance for designing interventions aimed at improving literacy skills of children exposed to socio-economic disadvantage. Results also highlight the importance of methods to investigate causal pathways from intervention to outcomes.

Seeing How it Sounds: Observation, Imitation and Improved Learning in Piano Playing

Research in various fields has shown that students benefit from teacher action demonstrations during instruction, establishing the need to better understand the effectiveness of different demonstration types across student proficiency levels. This study centres upon a piano learning and teaching environment in which beginners and intermediate piano students (N=48) learning to perform a specific type of staccato were submitted to three different (group exclusive) teaching conditions: audio-only demonstration of the musical task; observation of the teacher's action demonstration followed by student imitation (blockedobservation); and observation of the teacher's action demonstration whilst alternating imitation of the task with the teacher's performance (interleaved-observation). Learning was measured in relation to students' range of wrist amplitude (RWA) and ratio of sound and inter-sound duration (SIDR) before, during and after training. Observation and imitation of the teacher’s action demonstrations had a beneficial effect on students' staccato knowledge retention at different times after training: students submitted to interleaved-observation presented significantly shorter note duration and larger wrist rotation, and as such, were more proficient at the learned technique in each of the lesson and retention tests than students in the other learning conditions. There were no significant differences in performance or retention for students of different proficiency levels. These findings have relevant implications for instrumental music pedagogy and other contexts where embodied action is an essential aspect of the learning process.

Addressing the theory practice gap in nurse education: evaluating teaching through audit against NMC standards and final management placement

Introduction
This paper outlines an innovative approach to auditing and evaluating the content of a management and leadership module for undergraduate nursing students after their final management clinical placement. Normally evaluations of teaching in a module take place at the end of a teaching module and therefore do not properly reflect the value of the teaching in relation to practical clinical experience.
Aim
This audit and evaluation sought to explore both the practical value of the teaching and learning, and also the degree to which it the teaching reflected against the NMC Standards of Education and Learning (2010 domain 3).
Methods
Having piloted the evaluative tool with an earlier cohort of nursing students, this evaluation explored both a quantitative assessment employing a Personal Response System (n =172), together with a qualitative dimension (n=116), thus delivering paper-based comments and reflections from students on the value and practicality of the module teaching theory to their final clinical management experience. The quantitative audit data were analysed for frequencies and cross tabulation and the qualitative audit data were thematically analysed.
Results
Results suggest a significant proportion of the students, appreciated the quality of the standard of teaching, but more importantly, ‘valued or highly valued’ the teaching and learning in relation to how it helped to significantly inform their management placement experience. A smaller proportion of the students underlined limitations and areas in which further improvement can be made in teaching and learning to the module.
Conclusion
Significantly positive evaluation by the students of the practical value of teaching and learning, to the theoretical management module. This has proved a useful auditing approach in assessing the theoretical teaching to student’s Level 3 clinical experience, and facilitated significant recommendations as far as developing the teaching and learning to better reflect the practice needs of nursing students

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

FPGA-based soft-core processors for image processing applications

With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array (FPGA) has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary
progress on the design flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.