Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

The revenge of Gaia

Background
Conceptual understanding is a prerequisite for engineering competence. Concept maps may be effective tools for assessing conceptual knowledge, yet further work is needed to examine scoring methods.

Purpose
Our purpose was to evaluate the efficacy of three concept map scoring methods. Traditional scoring requires judges to count concept map components. Holistic scoring requires judges to analytically evaluate concept maps using a rubric. Categorical scoring requires judges to categorize concepts according to relevant categories before quantification of overall complexity.

Design/Method
In this study, concept maps were collected from 72 undergraduates before and after a capstone design course. Judges analyzed the concept maps using three methods. Using statistical analyses, we examined the comparability, interrater reliability, and convergent/divergent validity of scoring methods.

Results
While traditional scoring allowed judges to score concept maps relatively quickly, holistic scoring allowed judges to better capture changes in knowledge structure, because ranking of data was required without assuming hierarchical concept arrangement. Only categorical scoring provided insight into content and structure of student knowledge. For all methods, interrater reliability was acceptable, and convergent/divergent validity was established.

Conclusions
Due to its applicability to concept maps of varying structures, holistic scoring is best if more than one judge is available; traditional scoring is appropriate if there are time constraints or if multiple methods are used. Categorical scoring has potential as a supplemental or stand-alone method, depending on the needs of the instructor or researcher.

Assessing Conceptual Knowledge Using Three Concept Map Scoring Methods

Data processing systems and methods, according to various embodiments, perform privacy assessments and monitor new versions of computer code for updated features and conditions that relate to compliance with privacy standards. The systems and methods may obtain a copy of computer code (e.g., a software application or a website) that collects and/or uses personal data, and then automatically analyzes the computer code to identify one or more privacy-related attributes that may impact privacy assessment standards. In various embodiments, the system is adapted to monitor one or more locations (e.g., an online software application marketplace, and/or a specified website) to determine whether the application or website has changed. The system may, after analyzing the computer code, display the privacy-related attributes, collect information regarding the attributes, and automatically notify one or more designated individuals (e.g., privacy office representatives) regarding the attributes and information collected.

Data processing systems and methods for performing privacy assessments and monitoring of new versions of computer code for privacy compliance

Internet of Things (IoT) is increasingly used in a plethora of fields to enable radically new ways for various purposes, ranging from monitoring the environment to enhancing the wellbeing of human life With the ever-increasing size of such networks, it is fundamental to understand the issues that come with scaling on different networking layers A cost-efficient approach to examine large-scale networks is to use simulators or emulators to test the infrastructure and its ability to support the desired applications In this paper, we investigate and compare the currently available simulation/emulation software We found out that the current solutions are mostly appropriate for small- and medium-scale emulation, however they are not suitable for large-scale testing that reaches millions of node running concurrently We then propose a large-scale IoT emulator called MAMMotH and present a brief overview of its design Finally we discuss some of the current issues and future directions, eg radio link simulation

MAMMOTH: A massive-scale emulation platform for Internet of Things

While small topology simulations are important for validation and educational purposes, large-scale network simulations are a fundamental part of active networking research. Therefore, it is important for a network simulator to provide scalable and efficient solutions to execute these types of scenarios. Using standard sequential simulation techniques, large-scale topologies with substantial network traffic will require lengthy simulation execution times and a considerable amount of computer memory. Often, these execution times are too long for a networking researcher to run multiple simulations and collect significant data. Parallel and distributed simulation is one method that allows researchers to efficiently simulate these large topologies by distributing a single simulation program over multiple interconnected processors. To enable this scalable simulation methodology in ns-3, we formally present our distributed simulator, introduced in ns-3.8. This simulator uses the Message Passing Interface (MPI) standard and a conservative lookahead mechanism. Using the distributed simulator, we conducted a performance study using a large-scale point-to-point campus network scenario with a variable number of nodes distributed across several interconnected processors within a computer cluster. We show near-optimal improvements in simulation execution time are possible using the distributed simulator in ns-3.

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Distributed simulation with MPI in ns-3

Assessing impacts of a learning-cycle-based module on students' conceptual sustainability knowledge using concept maps and surveys

Embodiments of the disclosure are related to enforcing a policy on a computing device, or a companion device, based upon its proximity to another computing device, or an anchor device. In one example, the anchor device and companion device can report their location with respect to one another to a policy server. The policy server can determine whether the anchor device and proximity device are in proximity to one another as well as determine whether a policy should be applied to the companion device based upon whether it is in proximity to the anchor device.

Enforcement of proximity based policies

Dr. Mary Katherine Watson is currently an Assistant Professor of Civil and Environmental Engineering at The Citadel. Prior to joining the faculty at The Citadel, Dr. Watson earned her PhD in Civil and Environmental Engineering from The Georgia Institute of Technology. She also has BS and MS degrees in Biosystems Engineering from Clemson University. Dr. Watson’s research interests are in the areas of engineering education and biological waste treatment. Specifically, she has been involved in research projects to develop, refine, and apply innovative assessment tools for characterizing student knowledge of sustainability. Her ultimate goal is to use this assessment data to guide the design and evaluation of educational interventions to improve undergraduate sustainability education. In the area of bioprocessing, Dr. Watson has experience using bacteria and algae to convert waste materials into high-value products, such as biofuels.

Joshua Pelkey

Papers

Assessing Conceptual Knowledge Using Three Concept Map Scoring Methods

Distributed simulation with MPI in ns-3

Assessing impacts of a learning-cycle-based module on students' conceptual sustainability knowledge using concept maps and surveys

Enforcement of proximity based policies

Use of Concept Maps to Assess Student Sustainability Knowledge