Developing Measures of Content Knowledge for Teaching Reading

In this article we present results from a project to develop survey measures of the content knowledge teachers need to teach elementary reading. In areas such as mathematics and science, there has been great interest in the specialized ways teachers need to know a subject to teach it to others—often referred to as pedagogical content knowledge. However, little is known about what teachers need to know about reading to teach it effectively. We begin the article by discussing what might constitute content knowledge for teaching reading and by describing the survey items we wrote. Next, factor and scaling results are presented from a pilot study of 261 multiple‐choice items with 1,542 elementary teachers. We found that content knowledge for teaching reading included multiple dimensions, defined both by topic and by how teachers use knowledge in teaching practice. Items within these constructs formed reliable scales

Assessing Mathematical Knowledge for Teaching: The Role of Teaching Context

Assessments of mathematical knowledge for teaching (MKT), which are often designed to measure specialized types of mathematical knowledge, typically include a representation of teaching practice in the assessment task. This analysis makes use of an existing, validated set of 10 assessment tasks to both describe and explore the function of the teaching contexts represented. We found that teaching context serves a variety of functions, some more critical than others. These context features play an important role in both the design of assessments of MKT and the types of mathematical knowledge assessed

Payload-Directed Control of Geophysical Magnetic Surveys

Using non-navigational (e.g. imagers, scientific) sensor information in control loops is a difficult problem to which no general solution exists. Whether the task can be successfully achieved in a particular case depends highly on problem specifics, such as application domain and sensors of interest. In this study, we investigate the feasibility of using magnetometer data for control feedback in the context of geophysical magnetic surveys. An experimental system was created and deployed to (a) assess sensor integration with autonomous vehicles, (b) investigate how magnetometer data can be used for feedback control, and (c) evaluate the feasibility of using such a system for geophysical magnetic surveys. Finally, we report the results of our experiments and show that payload-directed control of geophysical magnetic surveys is indeed feasible

A Framework for Analysis of Case Studies of Reading Lessons

This paper focuses on the development and study of a framework to provide direction and guidance for practicing teachers in using a web-based case studies program for professional development in early reading; the program is called Case Studies Reading Lessons (CSRL). The framework directs and guides teachers’ analysis of reading instruction by focusing their attention to three critical dimensions of the process of teaching; in theory, analysis of a wide variety of reading lessons, using this framework, should contribute to teachers’ expertise. We report on a study of the Thinking Questions, which scaffold teachers’ analysis of the reading lessons, to determine the extent to which their responses meet theoretical expectations. Results suggest that teachers’ ratings of lessons tap their overall expertise in analysis of reading instruction, such that the three dimensions and features that represent these do not constitute separate factors. However, performance on the Thinking Questions differentiated more and less experienced teachers. As expected, less experienced teachers wrote longer and more specific comments about the instruction than more experienced teachers, who tended to highlight effective principles. The results suggest that an analytic framework of the kind used in CSRL holds promise as an effective component of a case-based professional development program. However, they also point to the need for further study of the framework and its influence on teachers’ own teaching practices

Using Remotely Piloted Aircraft and Onboard Processing to Optimize and Expand Data Collection

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs

Prototype Cryospheric Experimental Synthetic Aperture Radiometer (CESAR)

Present satellite microwave radiometers typically have a coarse spatial resolution of several kilometers or more. This is only adequate only over homogenous areas. Significantly enhanced spatial resolution is critically important to reduce the uncertainty of estimated cryospheric parameters in heterogeneous and climatically-sensitive areas. Examples include: (1) dynamic sea ice areas with frequent lead and polynya developments and variable ice thicknesses, (2) mountainous areas that require improved retrieval of snow water equivalent, and (3) melting outlet glacier or ice shelf areas along the coast of Greenland and Antarctica. For these situations and many others, an Earth surface spot size of no more than 100 m is necessary to retrieve the information needed for significant new scientific progress, including the synthesis of field observations with satellite observations with high confidence

Weathering the Storm: Unmanned Aircraft Systems in the Maritime, Atmospheric and Polar Environments

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs

Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3- and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study

Introduction: Over 70% of traumatic brain injuries (TBI) are classified as mild (mTBI), which present heterogeneously. Associations between pre-injury comorbidities and outcomes are not well-understood, and understanding their status as risk factors may improve mTBI management and prognostication.Methods: mTBI subjects (GCS 13–15) from TRACK-TBI Pilot completing 3- and 6-month functional [Glasgow Outcome Scale-Extended (GOSE)] and post-concussive outcomes [Acute Concussion Evaluation (ACE) physical/cognitive/sleep/emotional subdomains] were extracted. Pre-injury comorbidities >10% incidence were included in regressions for functional disability (GOSE ≤ 6) and post-concussive symptoms by subdomain. Odds ratios (OR) and mean differences (B) were reported. Significance was assessed at p < 0.0083 (Bonferroni correction).Results: In 260 subjects sustaining blunt mTBI, mean age was 44.0-years and 70.4% were male. Baseline comorbidities >10% incidence included psychiatric-30.0%, cardiac (hypertension)-23.8%, cardiac (structural/valvular/ischemic)-20.4%, gastrointestinal-15.8%, pulmonary-15.0%, and headache/migraine-11.5%. At 3- and 6-months separately, 30.8% had GOSE ≤ 6. At 3-months, psychiatric (GOSE ≤ 6: OR = 2.75, 95% CI [1.44–5.27]; ACE-physical: B = 1.06 [0.38–1.73]; ACE-cognitive: B = 0.72 [0.26–1.17]; ACE-sleep: B = 0.46 [0.17–0.75]; ACE-emotional: B = 0.64 [0.25–1.03]), headache/migraine (GOSE ≤ 6: OR = 4.10 [1.67–10.07]; ACE-sleep: B = 0.57 [0.15–1.00]; ACE-emotional: B = 0.92 [0.35–1.49]), and gastrointestinal history (ACE-physical: B = 1.25 [0.41–2.10]) were multivariable predictors of worse outcomes. At 6-months, psychiatric (GOSE ≤ 6: OR = 2.57 [1.38–4.77]; ACE-physical: B = 1.38 [0.68–2.09]; ACE-cognitive: B = 0.74 [0.28–1.20]; ACE-sleep: B = 0.51 [0.20–0.83]; ACE-emotional: B = 0.93 [0.53–1.33]), and headache/migraine history (ACE-physical: B = 1.81 [0.79–2.84]) predicted worse outcomes.Conclusions: Pre-injury psychiatric and pre-injury headache/migraine symptoms are risk factors for worse functional and post-concussive outcomes at 3- and 6-months post-mTBI. mTBI patients presenting to acute care should be evaluated for psychiatric and headache/migraine history, with lower thresholds for providing TBI education/resources, surveillance, and follow-up/referrals.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT01565551

Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3-and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study

Introduction: Over 70% of traumatic brain injuries (TBI) are classified as mild (mTBI), which present heterogeneously. Associations between pre-injury comorbidities and outcomes are not well-understood, and understanding their status as risk factors may improve mTBI management and prognostication. Methods: mTBI subjects (GCS 13–15) from TRACK-TBI Pilot completing 3- and 6-month functional [Glasgow Outcome Scale-Extended (GOSE)] and post-concussive outcomes [Acute Concussion Evaluation (ACE) physical/cognitive/sleep/emotional subdomains] were extracted. Pre-injury comorbidities >10% incidence were included in regressions for functional disability (GOSE ≤ 6) and post-concussive symptoms by subdomain. Odds ratios (OR) and mean differences (B) were reported. Significance was assessed at p < 0.0083 (Bonferroni correction). Results: In 260 subjects sustaining blunt mTBI, mean age was 44.0-years and 70.4% were male. Baseline comorbidities >10% incidence included psychiatric-30.0%, cardiac (hypertension)-23.8%, cardiac (structural/valvular/ischemic)-20.4%, gastrointestinal15.8%, pulmonary-15.0%, and headache/migraine-11.5%. At 3- and 6-months separately, 30.8% had GOSE ≤ 6. At 3-months, psychiatric (GOSE ≤ 6: OR = 2.75, 95% CI [1.44–5.27]; ACE-physical: B = 1.06 [0.38–1.73]; ACE-cognitive: B = 0.72 [0.26–1.17]; ACE-sleep: B = 0.46 [0.17–0.75]; ACE-emotional: B = 0.64 [0.25–1.03]), headache/migraine (GOSE ≤ 6: OR = 4.10 [1.67–10.07]; ACE-sleep: B = 0.57 [0.15–1.00]; ACE-emotional: B = 0.92 [0.35–1.49]), and gastrointestinal history (ACE-physical: B = 1.25 [0.41–2.10]) were multivariable predictors of worse outcomes. At 6-months, psychiatric (GOSE ≤ 6: OR = 2.57 [1.38–4.77]; ACE-physical: B = 1.38 [0.68–2.09]; ACE-cognitive: B = 0.74 [0.28–1.20]; ACE-sleep: B = 0.51 [0.20–0.83]; ACE-emotional: B = 0.93 [0.53–1.33]), and headache/migraine history (ACE-physical: B = 1.81 [0.79–2.84]) predicted worse outcomes. Conclusions: Pre-injury psychiat