Online reflective diaries - using technology to strengthen the learning experience

Internet based technologies have benefits for students and staff in terms of time and information sharing. Students at the University of Glasgow were required to engage in reflective writing, with tutor support, as part of their course assessment. We examine the benefits of this approach in fostering a deep, holistic approach to learning, student contribution to course development through this reflection, and the issues in support of these activities

In vivo functional and myeloarchitectonic mapping of human primary auditory areas

In contrast to vision, where retinotopic mapping alone can define areal borders, primary auditory areas such as A1 are best delineated by combining in vivo tonotopic mapping with postmortem cyto- or myeloarchitectonics from the same individual. We combined high-resolution (800 μm) quantitative T(1) mapping with phase-encoded tonotopic methods to map primary auditory areas (A1 and R) within the "auditory core" of human volunteers. We first quantitatively characterize the highly myelinated auditory core in terms of shape, area, cortical depth profile, and position, with our data showing considerable correspondence to postmortem myeloarchitectonic studies, both in cross-participant averages and in individuals. The core region contains two "mirror-image" tonotopic maps oriented along the same axis as observed in macaque and owl monkey. We suggest that these two maps within the core are the human analogs of primate auditory areas A1 and R. The core occupies a much smaller portion of tonotopically organized cortex on the superior temporal plane and gyrus than is generally supposed. The multimodal approach to defining the auditory core will facilitate investigations of structure-function relationships, comparative neuroanatomical studies, and promises new biomarkers for diagnosis and clinical studies

How Experienced High School Teachers Perceive the Effects of an Instructional Coaching Program on Their Pedagogical Strategies

A leading influence on teachers’ pedagogical strategies is the ongoing professional support provided by an instructional coaching program. However, due to competing needs, not all teachers receive the same amount of instructional coaching attention. More is known about the influence that instructional coaching programs have on new teachers and less about the benefits received by experienced teachers. The purpose of this study was to explore how experienced high school teachers perceive the effects of an instructional coaching program on their pedagogical strategies. Research questions also addressed how an instructional coaching program effected other areas of teacher performance and how teachers perceive the implementation of the program at their site. A hermeneutic phenomenological approach was used so that 10 participants could share their unique story with an instructional coaching program. Lewin’s theory on change management, Knowles’s ideas on adult learning, and Bandura’s self-efficacy model helped guide this study. From classroom observations, questionnaires, and interview responses, four major themes emerged: alternative coaching supports, improvement, leadership, and prioritization of duties. Results of this study revealed that all teachers positively perceive the concept of instructional coaching and most perceive that program implementation was working at their site, primarily for new teachers. However, results also showed that only a few experienced high school teachers perceive the coaching program to influence their pedagogical strategies. Findings from this study indicate that experienced teachers value coaching conversations to improve the quality of their pedagogical strategies

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of simple sum monetary aggregates and the Divisia indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although the traditional comparison of the series may suggest that they share similar dynamics, there are important differences during certain times and around turning points that can not be evaluated by their average behavior. We use a factor model with regime switching that offers several ways in which these differences can be analyzed. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each one series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors represent exactly where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We also find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginning and end of economic recessions, and during some high interest rate phases.Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the inferences’ policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia (1996) has observed in this regard, “measurement matters.”Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

ScotPID - a model of collaboration

ScotPID is a national personal development initiative in Scotland, with thirteen higher education institutions taking part in the development of case studies which enhance personal development planning for students. As a model of collaboration, ScotPID involves all stakeholders: each core project group is composed of an academic, IT support manager, careers service adviser and undergraduate student, with support from QAA Scotland. The case study is developed by the contribution of all of the members of the team. The strength of the ScotPID collaboration is the varied background of the team members. However, collaboration between the ScotPID teams should also be encouraged, to strengthen the inter-institutional approach further

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the policy relevance of the inferences. Indeed, as Belongia (1996) has observed in this regard, "measurement matters."Measurement error; monetary aggregation; Divisia index; aggregation; state space; Markov switching; monetary policy; index number theory; factor models

Salient sounds distort time perception and production

The auditory world is often cacophonous, with some sounds capturing attention and distracting us from our goals. Despite the universality of this experience, many questions remain about how and why sound captures attention, how rapidly behavior is disrupted, and how long this interference lasts. Here, we use a novel measure of behavioral disruption to test predictions made by models of auditory salience. Models predict that goal-directed behavior is disrupted immediately after points in time that feature a high degree of spectrotemporal change. We find that behavioral disruption is precisely time-locked to the onset of distracting sound events: Participants who tap to a metronome temporarily increase their tapping speed 750 ms after the onset of distractors. Moreover, this response is greater for more salient sounds (larger amplitude) and sound changes (greater pitch shift). We find that the time course of behavioral disruption is highly similar after acoustically disparate sound events: Both sound onsets and pitch shifts of continuous background sounds speed responses at 750 ms, with these effects dying out by 1,750 ms. These temporal distortions can be observed using only data from the first trial across participants. A potential mechanism underlying these results is that arousal increases after distracting sound events, leading to an expansion of time perception, and causing participants to misjudge when their next movement should begin

Big Data and Analysis of Data Transfers for International Research Networks Using NetSage

Modern science is increasingly data-driven and collaborative in nature. Many scientific disciplines, including genomics, high-energy physics, astronomy, and atmospheric science, produce petabytes of data that must be shared with collaborators all over the world. The National Science Foundation-supported International Research Network Connection (IRNC) links have been essential to enabling this collaboration, but as data sharing has increased, so has the amount of information being collected to understand network performance. New capabilities to measure and analyze the performance of international wide-area networks are essential to ensure end-users are able to take full advantage of such infrastructure for their big data applications. NetSage is a project to develop a unified, open, privacy-aware network measurement, and visualization service to address the needs of monitoring today's high-speed international research networks. NetSage collects data on both backbone links and exchange points, which can be as much as 1Tb per month. This puts a significant strain on hardware, not only in terms storage needs to hold multi-year historical data, but also in terms of processor and memory needs to analyze the data to understand network behaviors. This paper addresses the basic NetSage architecture, its current data collection and archiving approach, and details the constraints of dealing with this big data problem of handling vast amounts of monitoring data, while providing useful, extensible visualization to end users