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Strengthening Connections: The Effectiveness of Review Problems on Student Retention of Mechanics Concepts
Get PDFSpaced practice and interleaving concepts within a course improve student retention of those concepts. A metacognitive reason for the effectiveness of these techniques is the role they play in connecting concepts within a student’s knowledge structure. A previous paper by one of the authors summarized a recent experiment in which civil engineering students at the US Military Academy at West Point were required to solve review problems on each homework assignment in two civil engineering design courses. At that time, assessment data included three semesters of academic performance, time spent outside of class, student feedback, and teacher observations. In this paper, there are now six semesters of data from which to identify trends and two additional sources of assessment data are included: results from the Fundamentals of Engineering Exam and results from annual mechanics diagnostic exams administered in the first semester of the junior and senior year. Findings suggest that review problems improve performance in the course in which the review problems are assigned but the influence on longer term retention as measured by the annual mechanics exams and the FEE are inconclusive. Despite this, faculty and students find value in including review problems in structural design courses
Measuring the Return on Investment for AFRICOM’s African Enlisted Soldier Development Efforts II
No full textIn October 2018, AFRICOM published its Africa Enlisted Development Strategy (AEDS) to codify its approach for facilitating the professional development of its African partners. During Academic Year (AY) 2020, a senior capstone design team from USMA partnered with AFRICOM to create a scale to subjectively assess how well African nations develop and empower their non-commissioned officers (NCOs). Known as the Enlisted Development Maturity Level (EDML) scale, it provided AFRICOM with an easy-to-implement tool to evaluate the high-level return on its investments; however, it did not measure investment, and it lacked granularity. To remedy this, in AY 2021 USMA extended the EDML effort to track investments and score African militaries on six core NCO competencies. Dubbed the AEDS Investment Tracker and the Enlisted Development Review, they provide AFRICOM with a way to monitor the return on its investments over time, enhancing the implementation of the AEDS
System-Theoretic Requirements Defifinition for Human Interactions on Future Rotary-Wing Aircraft
No full textFuture rotary-wing aircraft designs are highly complex, optionally manned, and include advanced teaming concepts that create unknown human-automation interaction safety risks. System-Theoretic Process Analysis (STPA) enables analysis of hazards on these complex systems. This paper demonstrates how to apply STPA in future helicopters\u27 early concept development to prevent unacceptable losses. The system is modeled as a hierarchical control structure to capture interactions between components, including human and software controllers. Unsafe control actions are identified from these relationships and are used to systematically derive causal scenarios that arise from both hazardous interactions between system components and component failures. System requirements are then generated to mitigate these scenarios. A subset of the scenarios and requirements that address human factors related concerns are highlighted. Early identification of these problems helps designers (1) refine the concept of operations and control responsibilities and (2) effectively design safety into the system
Jack Voltaic 3.0 Cyber Research Report
Get PDFThe Jack Voltaic (JV) Cyber Research project is an innovative, bottom-up approach to critical infrastructure resilience that informs our understanding of existing cybersecurity capabilities and identifies gaps. JV 3.0 contributed to a repeatable framework cities and municipalities nationwide can use to prepare. This report on JV 3.0 provides findings and recommendations for the military, federal agencies, and policy makers
Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection
No full textAdversarial machine learning, a technique which seeks to deceive machine learning (ML) models, threatens the utility and reliability of ML systems. This is particularly relevant in critical ML implementations such as those found in Network Intrusion Detection Systems (NIDS). This paper considers the impact of adversarial influence on NIDS and proposes ways to improve ML based systems. Specifically, we consider five feature robustness metrics to determine which features in a model are most vulnerable, and four defense methods. These methods are tested on six ML models with four adversarial sample generation techniques. Our results show that across different models and adversarial generation techniques, there is limited consistency in vulnerable features or in effectiveness of defense method
Food for thought: A natural language processing analysis of the 2020 Dietary Guidelines publice comments.
No full textBACKGROUND: The Administrative Procedure Act of 1946 guarantees the public an opportunity to view and comment on the 2020 Dietary Guidelines as part of the policymaking process. In the past, public comments were submitted by postal mail or public hearings. The convenience of public comment through the Internet has generated increased comment volume, making manual analysis challenging.
OBJECTIVES: To apply natural language processing (NLP NLP is natural language processing.) to identify sentiment, emotion, and themes in the 2020 Dietary Guidelines public comments.
METHODS: Written comments to the Scientific Report of the 2020 Dietary Guidelines Advisory Committee that were uploaded and visible at https://beta.regulations.gov/docket/FNS-2020-0015 were extracted using a computer program and retained for analysis. All comments were filtered, and duplicates were removed. A 2-round latent Dirichlet analysis (LDA) was used to identify 3 overarching topics as well as subtopics addressed in the comments. Sentiment analysis was applied to categorize emotion and overall positive and negative sentiment within each topic.
RESULTS: Three different topics were identified by LDA. The first topic involved negative sentiment surrounding removing dairy from the guidelines because the commenters felt dairy is unnecessary. The second topic focused on positive sentiment involved in restricting added sugars. The third topic was too diverse to characterize under 1 theme. A second LDA within the third topic had 3 subtopics containing positive sentiment. The first subtopic valued the inclusion of dairy in the recommendations, the second involved the health benefits of consuming beef, and the third indicated that the recommendations lead to overall good health outcomes.
CONCLUSIONS: Public comments were diverse, held conflicting viewpoints, and often did not base comments on personal anecdotes or opinions without citing scientific evidence. Because the volume of public comments has grown dramatically, NLP has promise to assist in objective analysis of public comment input
Persuasive Features of Scientific Explanations: Explanatory Schemata of Physical and Psychosocial Phenomena
No full textExplanations are central to understanding the causal relationships between entities
within the environment. Instead of examining basic heuristics and schemata that inform
the acceptance or rejection of scientific explanations, recent studies have predominantly
examined complex explanatory models. In the present study, we examined which
essential features of explanatory schemata can account for phenomena that are
attributed to domain-specific knowledge. In two experiments, participants judged the
validity of logical syllogisms and reported confidence in their response. In addition to
validity of the explanations, we manipulated whether scientists or people explained an
animate or inanimate phenomenon using mechanistic (e.g., force, cause) or intentional
explanatory terms (e.g., believes, wants). Results indicate that intentional explanations
were generally considered to be less valid than mechanistic explanations and that
‘scientists’ were relatively more reliable sources of information of inanimate phenomena
whereas ‘people’ were relatively more reliable sources of information of animate
phenomena. Moreover, after controlling for participants’ performance, we found that
they expressed greater overconfidence for valid intentional and invalid mechanistic
explanations suggesting that the effect of belief-bias is greater in these conditions
A Methodology for Risk Assessment to Improve the Resilience and Sustainability of Critical Infrastructure with Case Studies from the United States Army
Get PDFReliable performance of energy and water infrastructure is central to the mission readiness of the United States Army. These systems are vulnerable to coordinated attacks from an adversary as well as disruption from natural events. The objectives of this work were to investigate Army installations in North America, identify best practices for improving the resilience and sustainability of critical energy and water infrastructure, and develop a framework and methodology for analyzing the resilience of an installation under varying outage scenarios. This work was accomplished using a multi-layered decision process to identify unique case studies from the 117 active-duty domestic Army installations. A framework for analyzing and assessing the resilience of an installation was then developed to help inform stakeholders. Metered energy and water data from buildings across Fort Benning, GA were curated to inform the modeling framework, including a discrete-event simulation of the supply and demand for energy and water on the installation using ProModel. This simulation was used to study the scale of solutions required to address outage events of varying frequency, duration, and magnitude, the combination of which is described as the severity of outages at a given site. This project helps develop a framework to inform how installations might meet Army Directive 2020-03, which states that installations must be able to sustain mission requirements for a minimum of 14 days after a disruption has occurred
Generating Genetic Engineering Linked Indicator Datasets for Machine Learning Classifier Training in Biosecurity
No full textAs methods and access to gene synthesis and genetic engineering have become more advanced, the fear that malicious viruses and bacteria will be designed with the express intention of causing harm to humans has received increased attention. In the event that such biological weapons are deployed, the security community needs tools to rapidly recognize the threat and identify responsible parties. Therefore, a key question is whether or not a biological threat is manmade. Currently, experts are capable of qualitatively assessing whether specific genetic sequences are natural or man-made, but few objective criteria exist for characterizing the degree to which a sequence has been engineered. Additionally, progress has recently been made on the task of attributing an engineered gene sequence to a lab-of-origin using machine learning. However, the task of analyzing naturally occurring genetic sequences so as to automatically detect outliers that may have been genetically engineered has received comparatively little attention. This work proposes a method for generating a dataset of natural and engineered sequences that can be used as an input for training machine learning classifiers to perform automatic detection of human engineering in gene sequence data