Dropout Model Evaluation in MOOCs

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions

Simple and Effective Multi-Paragraph Reading Comprehension

We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc

Balancing Government Risks with Contractor Incentives in Performance-Based Logistics Contracts

The use of Performance-Based Logistics (PBL) as a sustainment strategy for weapon systems has been mandated by the Department of Defense (DoD) and largely embraced by acquisition and contracting professionals in both government and private industry. Despite its apparent success, there is an inherent conflict that DoD implementers of PBL often face: the PBL goal of developing long-term partnerships that encourage investment from commercial partners is best achieved through lengthy, guaranteed contracts—but such contracts increase the DoD’s risk in an environment that is intended to transfer more risk to the contractor. This research examines issues associated with the type and length of PBL contracts between DoD organizations and private industry. The thesis addresses the question of how the DoD can ideally balance PBL contracts to mitigate operational and financial risks while simultaneously building long-term partnerships that encourage investment from commercial contractors. The results reveal five main areas in which the government should focus its efforts to improve PBL implementation

Semipermeable membrane devices are effective surrogates of fish in concentrating polychlorinated biphenyls

This study examined the effectiveness of Semipermeable Membrane Devices (SPMDs) as surrogates for fish in concentrating polychlorinated biphenyls. Golden shiners (Notemigonus crysolucas) and SPMDs were exposed to three different concentrations (0.5, 1.5, and 3.0ppm) of Aroclor 1254 for 1, 3, and 5 days under laboratory conditions. Concentrations of Aroclor 1254 were measured in the SPMDs and fish tissue using extraction techniques and gas chromatography. The concentrations of PCB in SPMDs and N. crysolucas were positively correlated. This relationship compared favorably with data from other studies. The relationship between the concentration of PCB in SPMDs and tissue offish and mollusks could be described by the equation F=2.38 S0.59, where F and S were the concentrations of PCB in fish and SPMDs (ng/g) respectively

In-Situ Colloidal MnO2 Deposition and Ozonation of 2,4-Dinitrotoluene

Laboratory experiments are presented that demonstrate a novel in situ semipassive reactive barrier for the degradation of 2,4 dinitrotoluene created by coating aquifer surfaces by deposition of colloidal MnO2, which catalyzes ozone degradation and enhances contaminant oxidation. Ozone is added to the reactive barrier and is transported through the zone with the contaminants by existing hydraulic gradients. The communication presents the preliminary laboratory investigation demonstrating the viability of this method. Studies were conducted by coating Ottawa sand with colloidal MnO2. Results show that concentrations of MnO2 in the range of 0.2 mg/g can be deposited with no measurable change in hydraulic conductivity, that there is significant coverage of the sand material by MnO2, and the deposition was not reversible under a wide range of chemical conditions. Ozonation of 2,4-dinitrotoluene in the presence of MnO2- coated sand was demonstrated to result in pseudo-first-order degradation kinetics with respect to DNT with half-lives ranging from 28 to 22 min (at pH 6 and 7, respectively), approximately 25% faster than experiments performed in the absence of MnO2