Students

The purpose of this explanatory sequential mixed methods study was to understand Native American students’ matriculation, retention, and lived experiences at Sunshine University (SU). Through a sequential design, academic profile, first-generation status, gender, age, campus involvement, enrollment status, and academic major were analyzed in how it predicts matriculation and retention of Native American students at SU. To provide a deeper understanding into Native American students at SU this study centered Native American students’ voices as it relates to their lived experiences in matriculating and persisting at SU.
This study used an explanatory sequential mixed methods design. Mixed methods research combines both qualitative and quantitative data collection and analyses (Creswell & Plano Clark, 2007; Ivankova et al., 2006; Merriam & Tisdell, 2016; Teddlie & Tashakkori, 2009). A sequential mixed methods design is conducted through sequences, in this study, phases, beginning with quantitative data collection and analyses and followed by qualitative data collection and analysis (Creswell & Plano Clark, 2007; Ivankova et al., 2006; Merriam & Tisdell, 2016; Teddlie & Tashakkori, 2009). This study was conducted in a three-phrase process: 1) quantitative data collection and analyses; 2) qualitative data collection and analysis; and 3) meta-inference and integration of the phases.

The computer industry does not understand how adults learn (Knowles, 1983). A profound statement made nearly 40 years ago. With the advancement of technology and the tremendous growth of online learning, the learning management system (LMS) has become the tool for delivering distance education. E-learning platforms have witnessed exponential uptake by the education and corporate sectors over the past three to five years (Wadhwani & Gankar, 2020). From this author's experience in the field of instructional design and online instruction, all LMSs are just a "database with a different user interface (Price, 2016)". But are there opposing perceptions from the online learner regarding two different systems of learning? With the migration of one LMS to another LMS at a large public state university, can an assumption be measured to determine significant differences between the two LMSs?
This quantitative research aims to answer if there are significant differences in online student perception between two different learning management systems. Using responses to Instructor Evaluation Forms during four academic years (12 semesters), this study determined if a significant difference existed between the perceived quality of two learning management systems. Moreover, this study added to a minimal body of research regarding improving the quality of learning management systems based on the perception of online students.

This mixed methods study sought to uncover the needs of culturally diverse students in the online learning environment. Several of the unexplored factors that may contribute to high attrition rate among online undergraduate students, were also analyzed. The study examined how the variables of prior educational experience, age, gender, ethnicity, country of birth, and first or native language spoken contributed to success in online classes. The research also explored how institutional support contributed to the success of online learners.
Through the use of survey data collection and interviews, the results of this study indicated that culturally diverse learners reported three skills that are essential to their success in online learning environments: time management, self-directedness, and computer or technical skills. Students also indicated that institutional and instructor support are vital to their success in online classes. Although all variables examined were not significant predictors to the success of online learners, the results of this study provide insight into the needs of culturally diverse learners. These findings may be helpful to educators and policymakers when planning for or designing online courses for culturally diverse learners. These findings may also aid in reducing the high attrition rate of culturally diverse learners in online environments by encouraging more readiness assessments for students enrolling in online courses to determine their level of readiness for online learning.

This study was conducted to investigate factors that contribute to resilience in students with learning disabilities (LD). The risk-resilience framework provided the theoretical base for selecting school and personal factors that might predict resilience. School and personal data were requested from large, culturally and linguistically diverse samples of individuals diagnosed with LD. A 12 variable model and three cluster models (combined variables) were developed. Discriminant analysis and tests of significance of hit rates were conducted to assess the accuracy of the full model (all 12 variables) to the prediction of resilience, and full versus restricted model testing was done to assess individual variable and cluster (combinations of some variables) contributions to the model. Additionally, analyses of environmental, intrapersonal, and interpersonal cluster models were investigated to determine their relative contribution to the prediction of resilience in relation to the others. Results of the full model analysis and subsequent tests of significance of hit rate indicated modest cross validated classification accuracy for the total group, resilient group, and non-resilient group. However, the model was not significantly better than chance, overall, at predicting resilience and non-resilience in students with LD. Results of the analysis of individual predictor variables’ and clusters’ contributions to the model’s classification accuracy indicated that no individual variable within the full model, nor cluster of interrelated variables contributed significant
incremental improvement in classification accuracy above and beyond that which is available from all other variables contained in the full model. The independent analysis of interrelated personal and school related factors clustered as environmental, interpersonal, and intrapersonal clusters revealed that, as unique and separate models, classification accuracy of cross-validated group cases were less than optimal for each cluster. The results further demonstrate that resilience is affected by both internal and external factors. Although the results also demonstrate that factors work together, a great deal is still to be learned regarding factors affecting resilience as well as their interplay in clusters of factors that affect resilience.