The English-as-a-second-language (ESL) community college student population has increased notably in the past decade, but a decreasing number of these students are completing courses, programs, or degrees (Erisman & Looney, 2008). These students come to college with unique background experiences, and once in college, deal with challenging linguistic, academic, and social integration issues. Though they are not linguistically homogenous, and they do not have a common purpose, ESL students share the common goal of attending community college to learn to speak English (Szelényi & Chang, 2002). Course completion is a primary measure of progress toward that goal, and is therefore an issue of concern for both ESL students and community colleges, which continue to be the access point for language-minority students progressing into higher education (Laden, 2004). The purpose of this study was to investigate the factors that predict in-term persistence of community college ESL students. A mixed methods research design consisting of two phases was utilized, and participants in this study were ESL students enrolled in a large community college in south Florida. Phase 1 students completed the Community College ESL Student Questionnaire (CCSEQ), which collected demographic data and data on entry characteristics, academic integration, and social integration. Discriminant and descriptive analyses were used to report the data collected in Phase I. Phase 2 students were a matching cohort of completing and non-completing students who participated in semi-structured interviews at the end of the term. Data collected in the interviews were analyzed thematically, using a constant comparative method as described by Glaser and Strauss (1967). Students’ self reported demographic data, background characteristics, goal commitment, and integration factors on the CCSEQ showed no significance between the students who completed the term and the students who did not complete the term. However, several differentiating themes emerged from the interview data, which indicated differences in goal commitment and integration between the two groups. The focus of non-completers on getting good grades rather than completing the course, and the commitment of completers to the goal of finishing the class in order to go forward, both raise questions for future research studies.

The hidden state Markov (HSM) model is introduced as a new conceptual framework for modelling long-term persistence in hydrological time series. Unlike the stochastic models currently used, the conceptual basis of the HSM model can be related to the physical processes that influence long-term hydrological time series in the Australian climatic regime. A Bayesian approach was used for model calibration. This enabled rigourous evaluation of parameter uncertainty, which proved crucial for the interpretation of the results. Applying the single site HSM model to rainfall data from selected Australian capital cities provided some revealing insights. In eastern Australia, where there is a significant influence from the tropical Pacific weather systems, the results showed a weak wet and medium dry state persistence was likely to exist. In southern Australia the results were inconclusive. However, they suggested a weak wet and strong dry persistence structure may exist, possibly due to the infrequent incursion of tropical weather systems in southern Australia. This led to the postulate that the tropical weather systems are the primary cause of two-state long-term persistence. The single and multi-site HSM model results for the Warragamba catchment rainfall data supported this hypothesis. A strong two-state persistence structure was likely to exist in the rainfall regime of this important water supply catchment. In contrast, the single and multi-site results for the Williams River catchment rainfall data were inconsistent. This illustrates further work is required to understand the application of the HSM model. Comparisons with the lag-one autoregressive [AR(1)] model showed that it was not able to reproduce the same long-term persistence as the HSM model. However, with record lengths typical of real data the difference between the two approaches was not statistically significant. Nevertheless, it was concluded that the HSM model provides a conceptually richer framework than the AR(1) model. / PhD Doctorate