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Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Statistical predictive models play an important role in learning analytics. In this work, we seek to harness the power of predictive modeling methodology for the development of an analytics framework in STEM student success efficacy studies. We develop novel predictive analytics tools to provide stakeholders automated and timely information to assess student performance toward a student success outcome, and to inform pedagogical decisions or intervention strategies. In particular, we take advantage of the random forest machine learning algorithm, proposing a number of innovations to identify key input thresholds, quantify the impact of inputs on student success, evaluate student success at benchmarks in a program of study, and obtain a student success score. The proposed machinery can also tailor information for advisers to identify the risk levels of individual students in efforts to enhance STEM persistence and STEM graduation success. We additionally present our predictive analytics pipeline, motivated by and illustrated in a particular STEM student success study at San Diego State University. We highlight the process of designing, implementing, validating, and deploying analytical tools or dashboards, and emphasize the advantage of leveraging the utilities of both statistical analyses and business intelligence tools in order to maximize functionality and computational capacity.

Estimating the efficacy of different instructional modalities, techniques, and interventions is challenging because teaching style covaries with instructor, and the typical student only takes a course once. We introduce the individualized treatment effect (ITE) from analyses of personalized medicine as a means to quantify individual student performance under different instructional modalities or intervention strategies, despite the fact that each student may experience only one “treatment.” The ITE is presented within an ensemble machine learning approach to evaluate student performance, identify factors indicative of student success, and estimate persistence. A key element is the use of a priori student information from institutional records. The methods are motivated and illustrated by a comparison of online and standard face-to-face offerings of an upper division applied statistics course that is a curriculum bottleneck at San Diego State University. The ITE allows us to characterize students that benefit from either the online or the traditional offerings. We find that students in the online class performed at least as well as the traditional lecture class on a common final exam. We discuss the general implications of this analytics framework for assessing pedagogical innovations and intervention strategies, identifying and characterizing at-risk students, and optimizing the individualized student learning environment.

Case. A 74-year-old female developed left shoulder pain after receiving an influenza vaccine. Her initial physical exam was suggestive of subacromial bursitis, and a corticosteroid injection into the subacromial space resulted in a 50% improvement in her pain. Subsequent MRI demonstrated myositis isolated to the infraspinatus muscle. She was successfully treated with anti-inflammatory medication and physical therapy. Conclusion. Shoulder injury related to vaccine administration (SIRVA) is a rare clinical complication, and myositis in the rotator cuff musculature has not been previously reported. Proper administration of intramuscular vaccinations should be emphasized to prevent injury to structures surrounding the shoulder joint.

The innervation of the adult abdominal heart of Drosophila melanogaster was studied by neuronal staining with green fluorescent protein and immunocytochemical techniques. The investigation was undertaken to determine whether the adult heart receives neuronal input or whether its complex activity must be considered independent from the nervous system. The larval heart lacks innervation, suggesting that the cardiac impulse is totally myogenic. At metamorphosis, segmental neural processes grow onto the myocardium. A pair of transverse nerves innervates bilaterally each cardiac chamber and its alary muscles. These nerve terminals are immunoreactive to glutamate and form unique synaptic structures on the ventral layer of longitudinal cardiac muscles of the conical chamber. This characteristic cardiac synapse may represent part of the neural mechanism controlling the retrograde heartbeat, and, thus, the cardiac reversal that is characteristic of adults. In addition, crustacean cardioactive peptide–immunoreactive fibers originating from peripheral, bipolar neurons (BpNs) fasciculate with the transverse nerve projections and terminate segmentally throughout the abdominal heart. An additional cluster composed of four large, CCAP-positive neurons innervates the terminal chamber. The cardioacceleratory effect of CCAP release at this location may modulate the properties of a pacemaker producing the anterograde heartbeat. J. Comp. Neurol. 465:560–578, 2003. © 2003 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]