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.