Academic Predictors of Online Course Success in the Community College

The purpose of this study was to identify academic factors that might predict online course success for community college students. Online course success was a focus of national research and debate as studies consistently indicated lower success rates in online courses as compared to traditional courses; however, research that identified academic predictors to guide the development of policies and services that support student success in online courses was limited. A random sample of 20 online course sections held at one multi-campus, urban community college resulted in 491 enrollees being examined for seventy-eight factors that might predict online course success. Factors present prior to online course enrollment included GPA; test scores; developmental coursework in reading, writing, and mathematics; college-level coursework in specific disciplines; and enrollment history. Factors present during the semester of online course enrollment included student status, current enrollment measures such as total number of courses attempted, total credits, and course duration. Demographic factors included gender, age, race/ethnicity, financial aid status, and geographic proximity to campus. Data extracted from the student registration system included demographic characteristics, course rosters, test scores, and enrollment history. Data were grouped into three blocks prior to analysis: demographics, academic factors prior to online enrollment, and academic factors during online enrollment. An unordered logistical regression evaluated the predictive value of these factors for online course success. Results of the logistical regression analysis indicated that the predictor model did not provide a statistically significant improvement over the constant-only model; the addition of variables did not improve the ability to predict the outcome, online course success. Continued analysis identified four statistically significant predictors of online course success in community college students. For factors measured prior to enrollment, cumulative college GPA was a positive predictor of online course success. For demographic factors, geographic proximity to campus was a negative predictor of online course success. For factors present during enrollment, total courses attempted (during the semester studied) was a positive predictor, and total credits attempted (during the semester studied) was a negative predictor of online course success. The researcher concluded that online course success in community college students was a complex issue that could not be explained by academic factors alone and suggested that future studies attempting to predict online course success in community college students be comprehensive in addressing the multitude of academic, social, and other factors that may influence online course success. Additional suggestions for further study included evaluating the relationship individual factors have to online course success and seeking out student perspectives regarding online courses to determine other factors that contribute to successful and unsuccessful online course experiences for community college students

Can Feature Predictive Power Generalize? Benchmarking Early Predictors of Student Success across Flipped and Online Courses

Early predictors of student success are becoming a key tool in flipped and online courses to ensure that no student is left behind along course activities. However, with an increased interest in this area, it has become hard to keep track of what the state of the art in early success prediction is. Moreover, prior work on early success prediction based on clickstreams has mostly focused on implementing features and models for a specific online course (e.g., a MOOC). It remains therefore under-explored how different features and models enable early predictions, based on the domain, structure, and educational setting of a given course. In this paper, we report the results of a systematic analysis of early success predictors for both flipped and online courses. In the first part, we focus on a specific flipped course. Specifically, we investigate eight feature sets, presented at top-level educational venues over the last few years, and a novel feature set proposed in this paper and tailored to this setting. We benchmark the performance of these feature sets using a RF classifier, and we provide and discuss an ensemble feature set optimized for the target flipped course. In the second part, we extend our analysis to courses with different educational settings (i.e., MOOCs), domains, and structure. Our results show that (i) the ensemble of optimal features varies depending on the course setting and structure, and (ii) the predictive performance of the optimal e

Predicting success for college students enrolled in an online, lab-based, biology course for non-majors

Online education has exploded in popularity. While there is ample research on predictors of traditional college student success, little research has been done on effective methods of predicting student success in online education. In this study, a number of demographic variables including GPA, ACT, gender, age and others were examined to determine what, if any, role they play in successfully predicting student success in an online, lab-based biology for non-majors course. Within course variables such as participation in specific categories of assignment and frequency of online visits were also examined. Groups of students including Native American/Non-Native American and Digital Immigrants and Digital Natives and others were also examined to determine if overall course success differed significantly. Good predictors of online success were found to be GPA, ACT, previous course experience and frequency of online visits with the course materials. Additionally, students who completed more of the online assignments within the course were more successful. Native American and Non-Native American students were found to differ in overall course success significantly as well. Findings indicate student academic background, previous college experience and time spent with course materials are the most important factors in course success. Recommendations include encouraging enrollment advisors to advise students about the importance of maintaining high academic levels, previous course experience and spending time with course materials may impact students' choices for online courses. A need for additional research in several areas is indicated, including Native American and Non-Native American differences. A more detailed examination of students' previous coursework would also be valuable. A study involving more courses, a larger number of students and surveys from faculty who teach online courses would help improve the generalizability of the conclusions

Are sores on the Grit-S questionnaire and class participation significant predictors of success in an online college mathematics course?

The Covid-19 pandemic significantly disrupted past norms in higher education. The immediate impact was felt worldwide, as an abrupt shift to remote learning made colleges more invested in online courses than ever before. Although not as evident, another significant change in higher education included a shift away from the use of traditional cognitive predictors (SAT and ACT scores) by colleges and universities, to admit prospective applicants. These trends escalate the need for faculty and administrators to identify non-cognitive predictors of achievement in online college courses, particularly in core subjects like mathematics. This study examined whether grit and participation are predictive of success in an online college mathematics course. Using scores from the Grit-S questionnaire and class participation, logistic regression analyses, a discriminant function analysis, and correlation analyses were carried out to identify statistically significant predictors of student success in online Intermediate Algebra courses at a community college in California. The online, synchronous mathematics courses were taught during the spring 2022 semester using Canvas, the college Learning Management System. Scores on the Grit-S questionnaire (a self-report ordinal 5-point survey with a total of 8 items), class participation (total activity time within Canvas), along with other factors (prior knowledge and age), were analyzed to identify the most influential predictors of success. The results suggest that class participation, one of the two components of grit (perseverance of effort), and prior knowledge are statistically significant predictors of success in an online college mathematics course. The identification of two non-cognitive, dispositional predictors of achievement (participation and grit) can aid college administrators and admissions professionals in reassessing the criteria used for admissions, while college faculty may concurrently develop and foster these traits in their iv courses. These findings may help colleges and universities draw diverse and highly qualified students, while simultaneously helping develop those currently enrolled into successful graduates

Learner Interest, Reading Comprehension and Achievement in Web-Based Learning

The web-based learning environment provides access to education for those who are unable to be physically present in a classroom. In situations where comprehensive learner analysis is cost prohibitive, fiscally prudent guidelines for learner analysis that include learner interest and the cultural attribute of language may be feasible alternatives to omitting learner analysis altogether as an online instructional design consideration. Community colleges routinely collect student data during the college admission process, such as the COMPASS reading score, which may be useful in predicting student success in web-based courses. Therefore, learner characteristics such as the COMPASS reading score, learner interest in course topic, and interest in web-based learning were examined to determine their utility as predictors of achievement in an online introductory health care course. Learner interests were measured using the Course Interest Scale and Web Interest Scale developed in 2008 by Nummenmaa and Nummenmaa. Simple and multiple regression analyses were utilized to determine potential associations. The results demonstrated that the COMPASS reading score positively predicted achievement and was statistically significant, F(1, 17) = 8.05, p = .011 when considered solely, when combined with course interest, F(2, 16) = 4.42, p = .030, and when combined with web interest, F(2, 16) = 3.79, p = .045. These findings indicated that the COMPASS reading score and other data routinely collected on community college students may be useful as predictors of success in online courses and may be effective for guiding student learning format design selections. Using familiar measures such as the COMPASS test score to predict achievement in web-based courses may promote learning outcomes, course completion rates, and graduation rates in community colleges.https://fuse.franklin.edu/ss2014/1037/thumbnail.jp

Online, Instructional Television And Traditional Delivery: Student Characteristics And Success Factors In Business Statistics

Distance education has surged in recent years while research on student characteristics and factors leading to successful outcomes has not kept pace. This study examined characteristics of regional university students in undergraduate Business Statistics and factors linked to their success based on three modes of delivery - Online, Instructional Television (ITV), and Traditional classroom. The three groups were found to have similar GPAs prior to taking their statistics courses. Online students were more likely to be repeating the course, to have earned more credit hours prior to enrolling, and to be significantly older. Ordinary Least Squares regression identified GPA and % absences (or an effort proxy) as highly significant predictors of course performance. Academic advisors are encouraged to suggest a traditional format to students who are repeating the course and to caution students that previous online coursework may produce expectations that are not appropriate for online courses in statistics

Predicting Student Success in Online Physical Education

Background/Purpose: Scholars have posited that the demand for online learning is not going away, and the question is no longer if online physical education (OLPE) is practical but rather, what are the most effective ways of administering OLPE to accommodate students (Daum & Buschner, 2012). Currently, limited data are available on student retention rates and attrition factors in OLPE courses. Several early OLPE studies (Brewer, 2001; Mosier, 2010; Ransdell et al., 2008) as well as the 2007 NASPE Initial Guidelines for Online Physical Education have suggested that certain prescreening efforts be in place prior to student enrollment in OLPE, however, at present no such empirically sound and theoretically based screening instruments exist. Screening and pre-screening systems can help identify students who are at risk of failing and/or not completing online coursework. The purpose of the study is to identify online student cognitive characteristics and environmental factors associated with success and/or failure within college online health-related fitness (HRF) courses. Methods/Analysis: Students (N=821) enrolled in Auburn University\u27s 16-week online HRF course---Active Auburn--- during the Fall 2017 participated in the study. At the beginning of the course, participants responded to two previously validated research instruments, the Educational Success Prediction Instrument Version-2 (ESPRI-V2; Roblyer, et al., 2008) and the Distance Learning Survey (DLS; Osborn, 2001). A Pearson\u27s Chi Square analysis was used for student demographic and environmental categorical data. Next, a one-way between subjects analysis of variance (ANOVA) was employed to compare completers and non-completers mean scores for each ESPRI-V2 and DLS cognitive factor (i.e. study environment). Lastly, a direct binary logistic regression was performed to assess the impact of significant factors from the previous analysis on the likelihood that student would complete or not complete an online HRF course. Results: The model contained 6 independent variables (GPA, class standing, hours worked outside of school, achievement, organization and study environment). The full model containing all predictors was statistically significant (&khgr; 2 (6, N=821) = 94.296, p\u3c.001), indicating that the model was able to distinguish between students who completed and did not complete the online HRF course. Four of the independent variables made a unique statistically significant contribution to the model: (1) GPA, (2) Class Standing, (3) Hours Worked Outside of School and (4) Organization. The strongest predictor of a course completion were student who reported entering the course with a GPA of 2.6- 4.0, recording an odds ratio of 3.96. This indicated that students who entered the course with a GPA above a 2.6 were almost 4 times more likely to complete an online HRF course than those who entered with a lower GPA, controlling for all other factors in the model. Conclusion: Upon course entry, students who did not complete the course generally reported a combination of the following factors: GPA below 2.6, worked more than 20 hours outside of school, underclassman class standing, and reported weak organizational beliefs. This analysis provides an initial understanding of the unique student characteristics affecting online HRF course completion

Comparing University Student Performance In Online V. Face-To-Face Offerings Of The Same Course, And Investigating Student Perceptions Of Satisfaction In An Online Course

Online education in the United States has seen dramatic growth for the past decade, outpacing any other growth in higher education. The concurrent mixed-methods study that was conducted for this research used data from a survey geology course taught in both environments, online and traditional face-to-face. The quantitative research focused on comparing student performance in an online course relative to the same face-to-face course, while the qualitative research investigated how students described their experiences taking an online class. Previous work in online education has been limited by relatively small sample sizes, conducting studies over just one semester, comparing dissimilar courses in one study, considering few of the stem disciplines, and, of the limited studies with GPA as a covariate, using self-reported GPA rather than actual GPA. The quantitative analysis of this study compared student performance in online (n=171) and face-to face (n=1266) environments using data from the same stem class over five years, with actual GPA as the covariate. Ancovas were calculated, and results shothat, overall, students performed better in the face-to-face class than in the online class, and this difference was more pronounced with students whose GPAs were 3.0 and lower. Ols regression was also conducted to identify predictors contributing to student success in the online classroom GPA, course load, and student credit hours were the only significant factors predicting online performance. For the qualitative component of this study, issues related to student satisfaction were explored by conducting a focus group from four students enrolled in the online stem course. Themes emerging from the discussion included interaction, technology, self-regulated learning practices, convenience, and course structure, with interaction as the most prominent theme. These findings help to explain the quantitative findings of why students with higher GPAs perform better- they do so, in part, because they have frequent interaction with the content despite the negative impact of the distance-based environment. Research, such as this study, is important in that identifying effective pedagogy promotes learning, particularly when the learning is done at a distance such as the online environment

Interactions in Online Courses and Student Academic Success

The Sloan Foundation reported a phenomenal 17% annual growth in online course enrollment from 2002 through 2012, while the overall enrollment in higher education has shown only a 2.5% annual growth. Despite the growth in enrollment, McFadden and others have reported that drop out rates were as much as seven times higher for online courses. To address these concerns, this study investigated how three types of interactions (student-student, student-content and student-instructor) in the first two weeks of fully asynchronous online courses were associated with student academic success. This investigation analyzed archived tracking data from a learning management system for 1,703 students in 200 semester-long, fully online community college courses. Multinomial logistic regression analysis was used to consider the relationship between the three types of online interactions and academic success. The three types of independent interaction variables were measured as follows. Student-student interaction was the number of student posts to the discussion forums. Student-content interaction was the total number of pages the students accessed. Student-instructor interaction was a measure of the number of instructor posts and the number of instructor emails. The outcome measure, academic success, fell into three groups: successful completers (students completing the course with A, B, or C grades), low score completers (students completing the course with D or F grades), and non-completers (students who did not complete the course). Odds ratios derived from the regression analysis were used to determine the percentage of change in academic success attributable to each type of interaction, holding all other factors constant. The multinomial logistic regression was statistically significant (chi square = 461.96 p<. 001), indicating that the predictors reliably distinguished between the three outcome groups. The findings suggest that increasing the number of times a student posts by one unit (5 posts) would increase the individual's odds for success by 74% for the non-completers group and by 71% for those in the low score completers group. The findings also suggest that if an individual would increase interaction with the content by one unit (49 online pages) the individual's likelihood for success would increase by 57% for non-completers and 39% for low score completers. The number of instructor posts had no effect on the outcome for low score completers, however increasing the number of instructor discussion posts by one unit (15 posts) increased the likelihood that the students would complete the course by 34%. An increase in one unit (151 messages) of instructor email was associated with a 45% decrease in the odds for success for non-completers and a 28% decrease in the odds for success for the low score completers. This study provides support that student-student interaction and student-content interaction in the first two weeks of online courses contribute to student academic success with student-student interaction being most influential. More instructor postings in the discussion areas increased the likelihood that students would complete the course. The finding that the more instructors interacted with students by email the lower the academic success seems counterintuitive at first glance. This may be because instructor emails are often in response to increased requests for clarification by students and could be a reflection of poor course organization or insufficient course support materials

Success in online credit recovery: Factors influencing student academic performance

Recent estimates show nearly 90% of school districts nationwide offer some form of online credit recovery. Additionally, credit recovery services have become one of the fastest growing areas of educational software. Despite the widespread adoption of these programs, there is a lack of scholarly research on the effectiveness, rigor, and suitability of online credit recovery. Given the popularity of online credit recovery and the mixed results that these programs have received, more study is imperative. Currently there is a dearth of research surrounding the suitability of online credit recovery for students. Much of the research conducted on virtual schooling indicates that the ideal student for this platform is autonomous, socially and emotionally mature, in possession of solid time management skills, and commands a developed internal locus of control. However, these characteristics are not typically embodied by the at-risk students primarily enrolled in online credit recovery courses. Given the disparity between the ideal online student and the typical recovery student, research examining the characteristics of students who have demonstrated success in online credit recovery could prove exceptionally beneficial. This study examined potential success factors of students enrolled in online credit recovery academic core discipline courses [English, Mathematics, Science, and Social Studies] within a school system in the mid-Atlantic region of the United States. The predictors of success in online credit recovery included student level variables: gender, race, grade-level, school discipline history, Individualized Educational Plan (IEP) status, Gifted & Talented (AIG) status, middle school state-standardized reading assessment results (reading EOG), middle school state-standardized mathematics assessment results (mathematics EOG), and middle school state-standardized science assessment results (science EOG). Student outcome (pass/fail) in the credit recovery course was the dependent variable. Descriptive statistics, chi-square, and binary logistic regression analysis were performed. Findings revealed that grade-level, IEP status, and middle school EOG results influenced outcomes in online credit recovery courses. Ancillary analyses revealed that underclassmen were less likely to achieve positive outcomes in science and social studies credit recovery courses compared to upperclassmen, and that results on 6-8th grade reading, mathematics, and science EOGs could have an influence on performance in science recovery courses. Possible implications of these findings are discussed