Student Characteristics As Predictors For Online Course Success

This study reviews the findings of previous empirical research published between the years of 2000 to 2009 on online effectiveness and student characteristics. A total of six research articles are identified and analyzed in terms of research design and findings. To improve online course effectiveness, research findings are summarized and analyzed. Some inconsistencies have been discovered and discussed

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

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

A comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks

This study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple regression and supervised artificial neural network (ANN) models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success

Self-Compassion, Psychological Resilience, and Social Media Use among Thai and British University Students

Previous research has suggested that self-compassion and psychological resilience are likely to be protective factors for young people’s psychological wellbeing during their time at university. However, no research has focused on self-compassion and psychological resilience among Thai and British students. The aims of this study were to explore the factors that affected self-compassion and psychological resilience among Thai and British university students and to explore the role that social media use has on these constructs. A total of 767 university students (482 Thai and 285 British undergraduate students) took part in a questionnaire-based study and 42 students (21 Thai and 21 British undergraduate students) participated in the in-depth interviews. The quantitative data show that gender, year of study as well as social media factors were predictors of self-compassion, while social support and perceived success influenced psychological resilience among Thai and British students. In addition, the qualitative data indicate that Thai and British students encountered similar problems and highlight the role that family and friends had on students’ strategies to deal with their problems compared to teachers and social media. The comparison between the two groups is discussed in relation to social media and cultural factors and the implications for higher education are considered

Predicting Success, Preventing Failure: An Investigation of the California High School Exit Exam

Examines early indicators that identify fourth-grade students in San Diego who are at risk of failing the California High School Exit Exam, discusses implications for when and how to intervene to address those factors, and makes policy recommendations