CORRECTIONAL POST‐SECONDARY EDUCATION: THE EXPANDING ROLE OF COMMUNITY COLLEGES

More than 75 percent of post‐secondary institutions offering education programs in prisons are community and vocational/ technical colleges. The predominance of community colleges among correctional education providers is a major change from the early 1970's when two‐thirds of such programs were provided by four‐year institutions.

Over 260 community colleges provide programs and services to the 26,000 inmates participating in higher education. Results of a survey on correctional post‐secondary education indicate that these programs are in many ways different from those educational institutions provide on their own campuses. For example, they generally, have different admission standards, are administered by part‐time staff, and rely upon part‐time and adjunct faculty.

Community and junior college administrators are urged to closely examine the operation and quality of their correctional programs and the need for evaluative research on the long‐term effects of these programs is also stressed.     

Comparisons of Four Methods for Estimating a Dynamic Factor Model

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a block-Toeplitz covariance matrix in the structural equation modeling framework. The third method is built in the Bayesian framework and implemented using Gibbs sampling. The fourth is the least squares method, which also employs the block-Toeplitz matrix. All 4 methods are implemented in currently available software. The simulation study shows that all 4 methods reach appropriate parameter estimates with comparable precision. Differences among the 4 estimation methods and related software are discussed.     

THE ENTANGLING WEB OF COMMITTEE USE IN GOVERNANCE

Learning a second language is often very difficult for many adults. This literature review points to a number of factors which may affect second language learning in adults. Teaching and program implications are suggested for community and junior college educators.     

Noncognitive Variables and Student Learning in Extended Opportunity Programs and Services (EOPS)

ABSTRACT

The purpose of this mixed methods study was to investigate the relationship between participation in Extended Opportunity Programs and Services (EOPS) at California Valley College (CVC) (one of the 113 California Community Colleges in California) and several measures of student learning. An embedded survey design was developed using William Sedlacek’s noncognitive questionnaire (NCQ), items from the Community College Survey of Student Engagement (CCSSE), items based on Marcia Baxter Magolda’s epistemological reflection model of cognitive complexity, and items based on student learning outcomes for learning in EOPS. The survey had a 43.7% response rate. Students’ demographic characteristics, progress data, and grades were also collected. The survey concluded with four open-ended questions examining students’ perceptions about their learning. GPA (grade point average) as an outcome measure, and several NCQ scales were positively correlated with the Learning in EOPS Scale. Qualitative and mixed methods analyses extended statistical findings, highlighting the importance of positive outlook, positive self-concept, realistic self-appraisal, and support for learning. Finally, the study found that students’ academic and social involvement played a noteworthy role in students’ development, especially as related to cognitive complexity and learning in EOPS.     

Doing it the Community College Way: Community College-Specific Programming

ABSTRACT

Community colleges are often boxed into university models despite the unique nature of their institutional missions and student populations. We believe community colleges need to develop their own models, rooted in their distinctive missions, to encourage student success. We illustrate this through our experience with a short-term research assistantship program. The program created a brief, but important, way to enhance the student–professor relationship and engage students in applied research practices. It also provided a resource to assist community college faculty in conducting their own, original research. We hope others will not only develop similar programs, but perhaps more importantly, that this article will spur bottom-up, creative thinking to engage time-constrained, community college students in meaningful ways.     

Binomial index of model fit: An elaboration

This article is an elaboration on the use of the binomial test of model fit value, which in this article will be referred to as the binomial index of model fit value, to gauge the degree that the data fit a path analytic or structural equation model. In addition, this article responds to the criticisms and comments made by Hsu (this issue), Drezner and Drezner (this issue), and Raykov and Penev (this issue) regarding the use of this approach to measuring the degree of model fit. We appreciate the comments provided by these authors. Their comments have assisted us in clarifying our reason for developing the binomial index of model fit procedure as well as our perception of its use.