REIMAGINING TECHNOLOGY PREPARATION FOR PRE-SERVICE TEACHERS: EXPLORING HOW THE USE OF A VIDEO SELF-ANALYSIS INSTRUCTIONAL COMPONENT, BASED ON THE EVIDENTIAL REASONING AND DECISION SUPPORT MODEL, IMPACTS PRE-SERVICE TEACHERS’ TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE

Teachers often teach on their own in their individual classrooms and thus have to mostly rely on themselves to reflect on their teaching practices and make improvements. This study explores how the use of a video self-analysis instructional component, based on the evidential reasoning and decision support model (ERDS), impacts pre-service teachers\u27 technological pedagogical content knowledge (TPACK). Using the explanatory sequential mixed methods design, the researcher first collected quantitative data. The collection of qualitative data then followed. This two-step process helped explain and elaborate on the quantitative results of this study. Participants in this study were 21 pre-service teachers enrolled in the third and final required technology integration courses during the 2016 fall semesters. Data sources used for this study included surveys, videotaped teaching samples, reflective essays, and semi-structured interviews. Results from the study indicate statistically significant improvements in participants’ self-perceptions towards their content knowledge (CK), pedagogical knowledge (PK), pedagogical content knowledge (PCK), technological content knowledge (TCK), technological pedagogical knowledge (TPK), and overall TPACK. Except for TK, the self-perception of all TPACK domains statistically significantly increased with medium to large effect sizes. Every participant in this study (n=21/21) cited that their ERDS guided video self-analysis was beneficial in informing their technology integration lesson planning process because the videos enabled them to observe their actual teaching practices. As a result, the pre-service teacher participants were able to critically assess their TPACK strength and limitations. In addition to changing participants’ TPACK perceptions, the participants also applied the lessons learned from their ERDS guided video self-analysis to actually change and improve their technology integration skills. For example, 85.7% (n=18/21) of the participants actually changed their instructional behaviors based on their self-prescribed action plan they outlined in their technology-enhanced lesson plans. The findings from this study suggest that the use of an ERDS guided video self-analysis instructional component was beneficial in helping pre-service teachers improve their ability to teach with technology because 1) it helped them challenge their own preconceptions of their TPACK; 2) enabled them to critique their own teaching and technology integration skills and; 3) provided them with authentic and accurate depictions of technology integration skills (e.g., videotaped lessons) so they could accurately prescribe a specific plan of action to improve their future technology-enhanced lessons. While this is only one study within a specific context, the results from this research suggest it may be worthwhile for scholars and teacher educators to continue examining the effects of using an ERDS guided video self-analysis instructional approach to improve teachers’ TPACK and technology integration skills

Fast nonlinear ion transport via field-induced hydrodynamic slip in sub-20-nm hydrophilic nanofluidic transistors

Electrolyte transport through an array of 20 nm wide, 20 μm long SiO_2 nanofluidic transistors is described. At sufficiently low ionic strength, the Debye screening length exceeds the channel width, and ion transport is limited by the negatively charged channel surfaces. At source−drain biases >5 V, the current exhibits a sharp, nonlinear increase, with a 20−50-fold conductance enhancement. This behavior is attributed to a breakdown of the zero-slip condition. Implications for energy conversion devices are discussed

Improving Classifier Confidence using Lossy Label-Invariant Transformations

Providing reliable model uncertainty estimates is imperative to enabling robust decision making by autonomous agents and humans alike. While recently there have been significant advances in confidence calibration for trained models, examples with poor calibration persist in most calibrated models. Consequently, multiple techniques have been proposed that leverage label-invariant transformations of the input (i.e., an input manifold) to improve worst-case confidence calibration. However, manifold-based confidence calibration techniques generally do not scale and/or require expensive retraining when applied to models with large input spaces (e.g., ImageNet). In this paper, we present the recursive lossy label-invariant calibration (ReCal) technique that leverages label-invariant transformations of the input that induce a loss of discriminatory information to recursively group (and calibrate) inputs - without requiring model retraining. We show that ReCal outperforms other calibration methods on multiple datasets, especially, on large-scale datasets such as ImageNet