Claire Wladis

Is the Second Time the Charm? Investigating Trends in Online Re-Enrollment, Retention and Success.

Online education is becoming an increasingly important component of higher education. The Sloan Foundation 2010 Survey of Online Learning reports that more than 30% of all students take at least one online course during their college career. Because of this, attention is now turning to the quality of student outcomes that this instructional method provides. However, there is a huge gap in empirical investigations devoted to the link between technology and performance indicators such as grade performance, re-enrollment and course completion (Nora & Plazas Snyder, 2008). This study found that prior online course experience is strongly correlated with future online course success. In fact, knowing a student’s prior online course success explains 13.2% of the variation in retention and 24.8% of the variation in online success in our sample, a large effect size. Students who have not successfully completed any previous online courses have very low success and retention rates, and students who have successfully completed all prior online courses have fairly high success and retention rates. Therefore, this study suggests that additional support services need to be provided to previously unsuccessful online learners, while students who succeed online should be encouraged to enroll in additional online courses in order to increase retention and success rates in online learning.

The Representation of Minority, Female, and Non-Traditional STEM Majors in the Online Environment at Community Colleges A Nationally Representative Study

Using data from more than 2,000 community college science, technology, engineering, and mathematics (STEM) majors in the National Postsecondary Student Aid Study, this research investigates how ethnicity, gender, non-traditional student risk factors, academic preparation, socio-economic status, and English-as-second-language/citizenship status relate to online course enrollment patterns. Even after controlling for other factors, Blacks and Hispanics (Black and Hispanic men, in particular) were significantly underrepresented in online courses, women were significantly overrepresented, and students with non-traditional student risk factors (delayed enrollment, no high school diploma, part-time enrollment, financially independent, have dependents, single-parent status, and working full-time) were significantly more likely to enroll online. However, although ethnicity, gender, and non-traditional factors were all important predictors for both 2- and 4-year STEM majors, at community colleges, ethnicity and gender were more important predictors of online enrollment than non-traditional characteristics, which is the opposite pattern observed at 4-year colleges.

The Online STEM Classroom--Who Succeeds? An Exploration of the Impact of Ethnicity, Gender, and Non-Traditional Student Characteristics in the Community College Context.

Objective: This study analyzes how ethnicity, gender, and non-traditional student characteristics relate to differential online versus face-to-face outcomes in science, technology, engineering, and mathematics (STEM) courses at community colleges. Method: This study used a sample of 3,600 students in online and face-to-face courses matched by course, instructor, and semester from a large urban community college in the Northeast. Outcomes were measured using rates of successful course completion (with a “C−” or higher). Multilevel logistic regression and propensity score matching were utilized to control for unobserved heterogeneity between courses and for differences in student characteristics. Results: With respect to successful course completion, older students did significantly better online, and women did significantly worse (although no worse than men) online, than would be expected based on their outcomes in comparable face-to-face courses. There was no significant interaction between the online medium and ethnicity, suggesting that though Black and Hispanic students may do worse on average in STEM courses than their White and Asian peers both online and face-to-face, this gap was not increased by the online environment. Contributions: These findings suggest that both women and younger students in STEM courses may need extra support in the online environment. Future research is needed (a) to explore whether factors such as stereotype threat or child care responsibilities affect the outcomes of women in online STEM courses, and (b) to determine which characteristics (e.g., motivation, self-directed learning skills) of older students may make them particularly well suited to the online environment.

An investigation of course-level factors as predictors of online STEM course outcomes

Abstract This study analyzed students who took STEM courses online or face-to-face at a large urban community college in the Northeastern U.S. to determine which course-level characteristics most strongly predicted higher rates of dropout or D/F grades in online STEM courses than would be expected in comparable face-to-face courses. While career and elective STEM courses had significantly higher success rates face-to-face than liberal arts and major requirement STEM courses respectively, career STEM courses had significantly higher success rates online than would be expected, while elective STEM courses had significantly lower success rates online than would be expected given the face-to-face results. Once propensity score matching was used to generate a matched subsample which was balanced on a number of student characteristics, differences in course outcomes by course characteristics were no longer significant. This suggests that while certain types of STEM courses can be identified as higher or lower risk in the online environment, this appears not to be because of the courses themselves, but rather because of the particular characteristics of the students who choose to take these courses online. Findings suggests that one potential intervention for improving online STEM course outcomes could be to target students in specific courses which are at higher risk in the online environment; this may allow institutions to leverage interventions by focusing them on the STEM courses at greatest risk of lower online success rates, where the students who are at highest risk of online dropout seem to be concentrated.

Which STEM majors enroll in online courses, and why should we care? The impact of ethnicity, gender, and non-traditional student characteristics

Using data from roughly 27,800 undergraduate STEM (science, technology, engineering and mathematics) majors in the National Postsecondary Student Aid Study (NPSAS), this research examines the relationship between race/ethnicity, gender and non-traditional student characteristics and online course enrollment. Hispanic and Black STEM majors were significantly less likely, and female STEM majors significantly more likely, to take online courses even when academic preparation, socioeconomic status (SES), citizenship and English-as-second-language (ESL) status were controlled. Furthermore, non-traditional student characteristics strongly increased the likelihood of enrolling in an online course, more so than any other characteristic, with online enrollment probability increasing steeply as the number of non-traditional factors increased. The impact of non-traditional factors on online enrollment was significantly stronger for STEM than non-STEM majors. Hispanic and Black STEM majors were significantly less likely to enroll online.Female STEM majors were significantly more likely to enroll online.STEM majors with non-traditional characteristics were more likely to enroll online.Non-traditional characteristics predict online enrollment more than other factors.Non-traditional characteristics were more significant for STEM than non-STEM majors.

Using course-level factors as predictors of online course outcomes: a multi-level analysis at a US urban community college

Research has documented lower retention rates in online versus face-to-face courses. However, little research has focused on the impact of course-level characteristics (e.g. elective versus distributional versus major requirements; difficulty level; STEM status) on online course outcomes. Yet, focusing interventions at the course level versus the student level may be a more economical approach to reducing online attrition. This study used multi-level modeling, and controlled for the effects of both instructor-level and student characteristics, to measure the relationship of course-level characteristics with successful completion of online and face-to-face courses. Elective courses, and to a lesser extent distributional course requirements, were significantly more likely to have a larger gap in successful course completion rates online versus face-to-face, when compared with major course requirements. Upper level courses had better course completion rates overall, but a larger gap in online versus face-to-face course outcomes than lower level courses.

Do online readiness surveys do what they claim? Validity, reliability, and subsequent student enrollment decisions

Online readiness surveys are commonly administered to students who wish to enroll in online courses in college. However, there have been no well-controlled studies to confirm whether these instruments predict online outcomes specifically (as opposed to predicting course outcomes more generally). This study used a sample of 24,006 students to test the validity and reliability of an online readiness survey similar to those used in practice at a majority of U.S. colleges. Multilevel models were used to determine if it was a valid predictor of differential online versus face-to-face course outcomes while controlling for unobserved heterogeneity among courses taken by the same student. Student self-selection into online courses was also controlled using student-level covariates. The study also tested the extent to which survey score correlated with subsequent decisions to enroll in an online course. No aspect of the survey was a significant predictor of differential online versus face-to-face performance. In fact, student characteristics commonly collected by institutional research departments were better predictors of differential online versus face-to-face course outcomes than the survey. Furthermore, survey score was inversely related to subsequent online enrollment rates, suggesting that the use of online readiness surveys may discourage some students from enrolling in online courses even when they are not at elevated risk online. This suggests that institutions should be extremely cautious about implementing online readiness surveys before they have been rigorously tested for validity in predicting differential online versus face-to-face outcomes. Online readiness surveys as currently implemented may have no predictive validity.Student characteristics may identify students at-risk online better than surveys.Readiness surveys may discourage the wrong students from enrolling online.Colleges should test readiness surveys for predictive validity before implementation.Colleges may want to consider alternatives to online readiness surveys.

Leveraging Technology to Improve Developmental Mathematics Course Completion: Evaluation of a Large-Scale Intervention

This study hypothesizes that course passing rates in remedial mathematics classes can be improved through early identification of at-risk students using a department-wide midterm, followed by a mandated set of online intervention assignments incorporating immediate and elaborate feedback for all students identified as at-risk by their midterm score. A sample of over 20,000 students was used to evaluate the intervention, which was implemented department-wide over several semesters in all developmental mathematics courses at a large diverse urban community college. The intervention was assessed by evaluating course passing rates (a proxy for passing rates on standardized exit examinations) and student time spent in the intervention lab. Students from semesters prior to the intervention were used as a control, with fall semesters compared to fall semesters and spring to spring, to control for possible variation in student enrollment. Highly statistically significant differences were found between student passing rates pre- versus post-intervention, with passing rates improving by as much as 50%. The size of this study and the diversity of the student population involved suggests that results are likely widely applicable to other institutions across the country. In particular, the interventions tested were chosen specifically because they can reasonably be implemented even across relatively large and diffuse departments with limited resources.

THE WORD PROBLEM AND THE METRIC FOR THE THOMPSON-STEIN GROUPS

We consider the Thompson-Stein group F(n1, ..., nk) where n1, ..., nk 2 {2,3,4, ...}, k 2 N. We highlight several differences between the cases k = 1 and k > 1, including the fact that minimal tree-pair diagram representatives of elements may not be unique when k > 1. We establish how to find minimal tree-pair diagram representatives of elements of F(n1, ..., nk), and we prove several theorems describing the equivalence of trees and tree-pair diagrams. We introduce a unique normal form for elements of F(n1, ..., nk) (with respect to the standard infinite generating set developed by Melanie Stein) which provides a solution to the word problem, and we give sharp upper and lower bounds on the metric with respect to the standard finite generating set, showing that in the case k > 1, the metric is not quasi-isometric to the number of leaves or caret in the minimal tree-pair diagram, as is the case when k = 1.

No time for college? An investigation of time poverty and parenthood

ABSTRACT Postsecondary outcomes are significantly worse for student parents even though they earn higher GPA’s on average. This study used institutional records and survey data from a large urban U.S. university to explore whether time poverty explains this trend. The results of regression and KHB decomposition analysis reveal that students with preschool-aged children have a significantly lower quantity and quality of time for college than comparable peers with older or no children, and that time spent on childcare is the primary reason for this difference. Both quantity and quality of time for education had a significant direct effect on college persistence and credit accumulation, even when controlling for other factors. Thus, greater availability of convenient and affordable childcare (e.g. increased on-campus childcare, revised financial aid formulas that include more accurate estimates of childcare costs) would likely lead to better college outcomes for students with young children.