Net gen or not gen? Student and Staff Evaluations of the use of Podcasts/Audio Files and an Electronic Voting System (EVS) in a Blended Learning Module.

At the authors’ institution, blended learning is defined as “educational provision where high quality e-learning opportunities and excellent campus-based learning are combined or blended in coherent, reflective and innovative ways so that learning is enhanced and choice is increased. Students are at the centre of this vision”. This paper outlines work undertaken to investigate the impact of integrating podcasts/audio file downloads and use of an electronic voting system (EVS) to transform module delivery from a traditional mode to a blended delivery. The purpose being to introduce a measure of flexibility in how, when and where students study; to increase interactivity and engagement in classroom sessions, and to enhance students' learning. The student cohort is diverse in respect of age – the majority or students are direct entry students of the so-called net generation, whilst a significant number of students (35%) are mature students. Would age be an influencing factor on the students’ preference for the learning methods employed, or their willingness or ability to engage with the technologies? An interim student evaluation was undertaken at the midpoint of the taught module, to provide formative, illustrative data to the module leader and teaching team about student opinion of the teaching methods and learning technologies. Given the option of returning to the traditional delivery method, 77.5% of students either “agreed” or “strongly agreed” that the module should continue to run in its blended format. The final evaluation discovered no discernable differences in the behaviour of the direct entry students compared to the mature students. Both groups accessed the podcasts easily, generally at home, and spent longer than if blended learning technologies had not been used. It was discovered that 16% of the mature and 24% of the direct entry students would have preferred lectures to podcasts, although the students were positive about the flexibility offered. Both groups of students were virtually unanimous on the benefits of the EVS to support learning. The teaching team concluded that the blended learning technologies increased the students’ engagement with their learning

Incorporating learning technologies into undergraduate radiography education

Original article can be found at: http://www.sciencedirect.com/science/journal/10788174 Copyright The College of RadiographersThis study investigated the impact of integrating podcasts/audio file downloads and use of an electronic voting system (EVS) on a previously traditionally taught module. Both student (direct entry and mature) and staff satisfaction with the modified structure were evaluated.Peer reviewe

Indium foil with beryllia washer improves transistor heat dissipation

Indium foil, used as an interface material in transistor mountings, greatly reduces the thermal resistance of beryllia washers. This method improves the heat dissipation of power transistors in a vacuum environment

Perceptions of Probation Officers Around Class and Racial Disparities in the Juvenile Justice System

The pervasiveness of disparities related to race and class is an important topic in the juvenile justice systems. The current research examines perceptions of juvenile probation officers around disparities related to race and class in the juvenile justice system. A number of theoretical and methodological approaches are discussed in the literature review. A conceptual framework of intersectionality is used as an analytic technique to examine the simultaneous interplay of race and class and its impact on disparities related to race and class in the juvenile justice system. The sample of juvenile probation officers has been drawn from a department of corrections for a county employer located in an urban community with the Midwestern United States. A total of 17 juvenile probation officers responded to the 24-item survey. Descriptive and inferential statistics were generated for the collected data. Chi-square analyses were generated to examine the associations between the levels of agreeableness for variables. The findings yielded minimal contributions to the current research due to the low amount of participants. However, despite the low amount of participants, there were two significant associations between variables. The findings had implications for practice, policies, and research in the fields of social work and corrections. The limitations to this current research encourage new research designs capturing greater participation rates while the strengths provide groundwork for future research capturing data regarding disparities related to race and class in the juvenile justice system

Preparation of ultracold atom clouds at the shot noise level

We prepare number stabilized ultracold clouds through the real-time analysis of non-destructive images and the application of feedback. In our experiments, the atom number ${N\sim10^6}$ is determined by high precision Faraday imaging with uncertainty $\Delta_N$ below the shot noise level, i.e., $\Delta_N <\sqrt{N}$. Based on this measurement, feedback is applied to reduce the atom number to a user-defined target, whereupon a second imaging series probes the number stabilized cloud. By this method, we show that the atom number in ultracold clouds can be prepared below the shot noise level.Comment: Main text: 4 Figures, 4 pages. Supplemental Information: 4 figures, 5 page

Scoring a forced-choice image-based assessment of personality: A comparison of machine learning, regression, and summative approaches

Recent years have seen rapid advancements in the way that personality is measured, resulting in a number of innovative predictive measures being proposed, including using features extracted from videos and social media profiles. In the context of selection, game- and image-based assessments of personality are emerging, which can overcome issues like social desirability bias, lack of engagement and low response rates that are associated with traditional self-report measures. Forced-choice formats, where respondents are asked to rank responses, can also mitigate issues such as acquiescence and social desirability bias. Previously, we reported on the development of a gamified forced-choice image-based assessment of the Big Five personality traits created for use in selection, using Lasso regression for the scoring algorithms. In this study, we compare the machine-learning-based Lasso approach to ordinary least squares regression, as well as the summative approach that is typical of forced-choice formats. We find that the Lasso approach performs best in terms of generalisability and convergent validity, although the other methods have greater discriminate validity. We recommend the use of predictive Lasso regression models for scoring forced-choice image-based measures of personality over the other approaches. Potential further studies are suggested