While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months.

Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed).

The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.

This action research study is a mixed methods investigation of doctoral students’ preparedness for multiple career paths. PhD students face two challenges preparing for multiple career paths: lack of preparation and limited engagement in conversations about the value of their research across multiple audiences. This study focuses on PhD students’ perceived perception of communicating the value of their research across academic and non-academic audiences and on an institutional intervention designed to increase student’s proficiency to communicate the value of their PhD research across multiple audiences. Additionally, the study identified ways universities can implement solutions to prepare first-generation PhD students to effectively achieve their career goals.

I developed a course titled Preparing Future Scholars (PFS). PFS was designed to be an institutional intervention to address the fundamental changes needed in the career development of PhD students. Through PFS curricula, PhD students engage in conversations and have access to resources that augment both the traditional PhD training and occupational identity of professorate. The PFS course creates fundamental changes by drawing from David Kolb’s Experiential Learning Theory and the Social Cognitive Career Theory (SCCT) developed by Robert Lent, Steven Brown, and Gail Hackett. The SCCT looks at one’s self-efficacy beliefs, outcome expectations, goal representation, and the interlocking process of interest development, along with their choice and performance.

I used a concurrent triangulation mixed methods research model that included collecting qualitative and quantitative data over 8 weeks. The results of the study indicated that PhD students’ career preparation should focus on articulating the relevancy of their research across academic and non-academic employment sectors. Additionally, findings showed that PhD students’ perception of their verbal and non-verbal skills to communicate the value of their research to both lay and discipline specific audiences were not statistically different across STEM and non-STEM majors, generational status, or gender, but there are statistical differences within each group. PhD programs provide students with the opportunity to cultivate intellectual knowledge, but, as this study illustrates, students would also benefit from the opportunity to nurture and develop practical knowledge and turn “theory into practice.”

More than 30% of college entrants are placed in remedial mathematics (RM). Given that an explicit relationship exists between students' high school mathematics and college success in science, technology, engineering, and mathematical (STEM) fields, it is important to understand RM students' characteristics in high school. Using the Education Longitudinal Survey 2002/2006 data, this study evaluated more than 130 variables for statistical and practical significance. The variables included standard demographic data, prior achievement and transcript data, family and teacher perceptions, school characteristics, and student attitudinal variables, all of which are identified as influential in mathematical success. These variables were analyzed using logistic regression models to estimate the likelihood that a student would be placed into RM. As might be expected, student test scores, highest mathematics course taken, and high school grade point average were the strongest predictors of success in college mathematics courses. Attitude variables had a marginal effect on the most advantaged students, but their effect cannot be evaluated for disadvantaged students, due to a non-random pattern of missing data. Further research should concentrate on obtaining answers to the attitudinal questions and investigating their influence and interaction with academic indicators.