The Shifting Gears Approach to Systems Change

Evaluates the Shifting Gears initiative to strengthen state postsecondary, adult basic education, and skills development systems through data, policy change, engagement, and communications. Focuses on the logic model and the policy agenda and action plan

Strengthening State Financial Aid Policies for Low-Income Working Adults

Explains the need to expand state financial aid programs to help the working poor enroll in college. Recommends funding new aid for working adults as well as strengthening existing aid to meet their needs, and describes recent state initiatives

Improving Student Success by Strengthening Developmental Education in Community Colleges: The Role of State Policy

Outlines the need to strengthen community college students' basic English and math skills as required for college courses in order to meet workforce needs. Describes promising approaches to improving developmental education and recommends state policies

Design and development of a Novel Soft Gripper Manipulated by a Robotic Arm

This study presents the design and development of a tendon-driven soft gripper manipulated by a 4 DOF robotic arm. The proposed robotic arm and the gripper explore the new areas focusing on increasing the grasping performance of the gripper as well as the workspace. The gripper is designed as a 3 finger and driven by tendons using two servo motors. The tension of the strings is adjusted using a pulley mechanism and a string. The opening and grasping of the soft gripper are accomplished by each motor. The wide opening allows the gripper to grasp wide objects. The parallel robotic arm motion is actuated by 4 motors. Each motor is mounted on a spherical shoulder plate while circular plates with angles axle extrusions are also attached to the motors. The axles are angled so that their axes of rotation converge to the center of the shoulder plate. The vertical and lateral motion of the robotic arm is controlled by the series linkages connected to the axles, thereby actuating the forearm of the mechanism. The robotic arm is 3D printed in polylactic acid (PLA) and the single piece designed soft gripper is 3D printed in thermoplastic polyurethane (TPU). The gripping force applied by the gripper is obtained using flexible sensors attached to the tip of the 3 fingers. The finite element analysis is performed in Ansys and the link lengths are optimized to trace the desired workspace. The mechanism is tested for its grasping and lifting of various objects showing promising superiorities in terms of its grasping capabilities mimicking the human hand. If the robotic arm is mounted on a moving platform, then it can serve as an assistive robot for the elderly

The Future of Private Loans: Who is Borrowing, and Why?

Examines developments in private loans within the student lending industry, characteristics of loan borrowers, and trends that might impact the growth of private loans in the future

Merit Aid and Inequality: Evidence from Baccalaureate & Beyond

This study examines the distribution of merit-based and need-based financial aid packages among a nationally representative sample of baccalaureate degree recipients. The results show that students from underrepresented race and class backgrounds are less likely to receive merit aid and that students with minimal financial need are more likely to receive merit aid. Because the majority of financial aid flows through student loan programs, shifting financial aid resources toward merit-based scholarships may increase the costs of higher education for students with high financial need. Thus, awarding financial aid regardless of student need is counterproductive to the goal of equal opportunity in postsecondary education

Life, Death and Preferential Attachment

Scientific communities are characterized by strong stratification. The highly skewed frequency distribution of citations of published scientific papers suggests a relatively small number of active, cited papers embedded in a sea of inactive and uncited papers. We propose an analytically soluble model which allows for the death of nodes. This model provides an excellent description of the citation distributions for live and dead papers in the SPIRES database. Further, this model suggests a novel and general mechanism for the generation of power law distributions in networks whenever the fraction of active nodes is small.Comment: 5 pages, 2 figure

Creating Opportunity for All: Building Pathways from Continuing Education to Credit Programs

In 2013, Achieving the Dream convened seven network colleges to form the Northeast Resiliency Consortium (NRC). Created in the wake of natural and manmade disasters, the consortium sought to develop a resilient workforce. Led by Passaic County Community College, and including Atlantic Cape Community College, Bunker Hill Community College, Capital Community College, Housatonic Community College, Kingsborough Community College, and LaGuardia Community College, the consortium was awarded $23.5 million from the US Department of Labor's Trade Adjustment Assistance Community College Career Training (TAACCCT) program. Achieving the Dream served as the consortium's convening partner and as an intermediary to support peer learning among colleges, provide technical assistance, host in-person consortium convenings, and promote promising strategies implemented by the consortium. One important challenge the NRC colleges took on was aligning continuing education and credit programs along a career pathway to meet student and labor market needs. This brief describes how the colleges made adjustments to articulate non-credit to credit credentials to ensure strong career pathways for students who start on the non-credit ramp.As a result, the colleges are now providing students with stacked credentials, ensuring prior learning and experience is accounted for within pathways, and are formally recognizing key milestones with credentials. All in all, the colleges smoothed the way for students to work toward an associate degree even if they began their studies in a non-credit program

Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE