Is More Better? The Impact of Postsecondary Education on the Economic and Social Well-Being of American Society

Provides a review of research literature that examines the impact of higher education on individuals and society. Looks at economic and non-economic benefits and costs associated with an increase in public investment in postsecondary education

A different viewpoint on student retention

Although student retention, persistence, and graduation is a high priority for institutions and policymakers, graduation rates are not improving. Nowadays, more students from first-generation and low-income backgrounds have access to traditional higher education. In this essay, the author argues that an educational system that fails to prepare many students for higher education and the growing costs of attending college are making it more and more difficult for many students to persist and graduate. He concludes by stating that ultimately, we might need to decide, on a policy basis, who we want to go to college, who we want to succeed, and who will pay for it.DOI: 10.18870/hlrc.v4i2.21

Latino Youth and the Pathway to College

Outlines the pathway to and through postsecondary education for Latinos, and looks at a number of variables that offer insight into how motivated and prepared these students are for postsecondary work

Latino High School & Baccalaureate Graduates: A Comparison

Part of a series that documents the challenges facing Latino students as they progress through the educational system. Examines the primary differences between Latino and white students for those who completed a BA and other levels of education

Sponsors of Early Intervention Programs

Learning about early intervention programs can be a challenge for parents and students not only because programs are so small—programs administered by individual colleges and universities serve a median of 82 students (Chaney, Lewis, and Farris, 1995)—but also because of the wide variation in the types of organizations that sponsor such programs. Although this variety can make learning about programs difficult, it also helps ensure that, once existing programs are identified and located, a student will find a program that is well suited to his or her individual needs and characteristics. Unfortunately, no comprehensive directory, compendium, or national clearinghouse of early intervention programs has been developed. However, this article does provide a brief overview of the early intervention programs that are sponsored by private organizations and foundations; the federal government; federal, state, and local government collaborations; schoolcollege collaborations; and colleges and universities

Pre-College Outreach and Early Intervention

Both individuals and society at large benefit when an individual earns a college degree. The benefits to individuals are short term and long term, economic and non-economic. Short-term benefits include enjoyment of the learning experience, participation in athletic, cultural, and social events, and enhancement of social status. Long-term benefits include higher lifetime earnings, more fulfilling work environment, better health, and longer life.1 Although societal benefits are more difficult to quantify, benefits that spill over beyond the individual cannot be ignored.2 One societal benefit is the economic growth associated with the enhanced productivity of labor resulting from higher levels of educational attainment. Neighborhood effects are another societal benefit. These include reduced crime, reduced dependency on public welfare and Medicaid, increased volunteerism, greater voting rates, and increased levels of civic involvement. The single most important effect of higher education may well be intergenerational–manifested, for example, in the increased educational attainment of one’s children.3 For the individual and societal benefits of higher education to be realized, individuals must have the opportunity and ability to access postsecondary education and persist to degree completion

From Middle School to the Workforce: Latino Students in the Educational Pipeline

Part of a series that documents the challenges facing Latino students as they progress through the educational system. Analyzes 1988-2000 NELS data to track students from eighth grade through high school, postsecondary education, and on to the workforce

Pathways to the Bachelor's Degree for Latino Students

Part of a series that documents the challenges facing Latino students as they progress through the educational system. Focuses on students who attained a BA, and the major factors that had an impact on their ability to navigate the educational system

Enhancing Lightpath QoT Computation with Machine Learning in Partially Disaggregated Optical Networks

Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented