Predicting Student Success in Online Courses at a Rural Alabama Community College
Predicting Student Success in Online Courses at a Rural Alabama Community College

Author: Leslie Ann Cummings

Publisher:

Published: 2009

Total Pages:

ISBN-13:

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Community colleges have utilized distance education to reach previously underserved populations. Considering the educational opportunities afforded by increased Internet access and the history of community colleges of providing open access to all individuals, it is no wonder that distance education has grown as a means of extending education in rural areas. Along with taking advantage of these opportunities, community colleges must also be committed to the success of students in the online environment. There is a need to identify individual student characteristics that predict success in the online environment in order to provide appropriate course enrollment advising. This study examines demographic and educational variables of online students at Bevill State Community College, with the goal of identifying the predictive ability of student characteristics on success in online courses. Online learners at Bevill State were mostly females and roughly half had completed an introductory computer course before enrolling in an online class. The average age of the participants was 25.57. These individuals had an average GPA of 3.07 and had completed an average of 4.56 semesters of college. Overall, 71.1% of the participants were successful in the online course in which they were enrolled, having achieved a grade of D or higher. The logistic regression model of five predictor variables was 72% accurate in predicting student success and non-success. Results show that the major factors influencing whether a person is successful in online classes are: age at the time of enrollment, overall GPA before enrollment, and the number of semesters of previous college experience. These findings indicate that students who are older, have more experience in college, and who have had more success in the traditional classroom may be more likely to be successful in the online environment. As online education continues its growth, identifying factors that help to distinguish between those who may be successful and those who may not will help students, advisors, and administrators make informed decisions about course enrollments. Future research should include a variety of methodologies to further explore the variables identified here as well as others that may influence student success in the online environment.

Success Factors Among Community College Students in an Online Learning Environment
Success Factors Among Community College Students in an Online Learning Environment

Author: Paula B. Doherty

Publisher: Universal-Publishers

Published: 2000-08-16

Total Pages: 238

ISBN-13: 1581121067

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Little is known about student success in online learning environments, especially how the predisposing characteristics that the learner brings to the learning environment may differentially affect student outcomes. This study explored the question of whether a student's "readiness" to be a self-directed learner is a predictor of student success in an online community college curriculum. The specific goal of this investigation was to determine whether there was a significant relationship between self-directed learning readiness-as measured by Guglielmino's (1977) Self-Directed Learning Readiness Scale (SDLRS)- and student success-as measured by course completion, grade point average (GPA) and student satisfaction, the latter assessed by student responses to an opinion poll. The subjects of this study were community college students in the state of Washington, enrolled in one or more transfer-level online courses delivered via WashingtonONLINE (WAOL) during fall quarter 1999. Students who voluntarily chose to respond to two elective surveys comprised the study sample. A correlational research design was used to test the explanatory power of self-directed learning readiness and to describe the relationships between variables. Since this study was designed to test hypothesized relationships, the resulting correlation coefficients were interpreted in terms of their statistical significance. The expected outcome of this study was to confirm or disconfirm a statistically significant relationship between self-directed learning readiness and student success in an online community college curriculum. The findings of this study failed to achieve this outcome due to (1) the lack of statistical reliability of the SDLRS among the subject population; (2) the resulting lack of validity of the SDLRS among the study sample; (3) a nonresponse effect; and (4) a self-selection effect. The unanticipated outcome of this study was evidence that student perception of student/instructor interactions is a single variable predictor of student success among community college students in an online learning environment. Recommendations for further study include Web-specific research methodologies that address the potentially deleterious effects of nonresponse and self-selection in cyber-research environments and continued exploration of the multiple facets of student success in asynchronous learning domains.

Predicting Student Success in Coursework Within a Regional Online School
Predicting Student Success in Coursework Within a Regional Online School

Author: Cary J. Stamas

Publisher:

Published: 2021

Total Pages: 137

ISBN-13:

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Online education options in the K-12 environment have steadily increased from the infancy of online education at the turn of the millennia. Educators have utilized this format to meet the many different needs that exist for all students. Early research into the academic success of students in these environments prior to 2000 indicated there was no significant difference in student achievement for distance learning as compared to face-to-face learning. Since 2000, there has been increased focus on student performance in higher education online environments, but research is limited for K-12 schools. For the research that does exist, school-level variables and the reasons why students select online environments have not been investigated. This study examines the within-school and between-school factors that predict the performance of students in online environments utilizing hierarchical linear modeling (HLM). The data sample represents information from a regional online school (ROS) that enrolls 9-12 students in online coursework from local schools in the region. The sample included 886 students from 36 local schools. The student-level variables that were investigated included prior student performance, special education status, student free or reduced-price lunch status, race, gender, age, and the reason for selecting online coursework. The school-level variables included in the analyses were school enrollment, percentage of students who qualify for free or reduced-price lunch, school average SAT score, percentage of Black students enrolled, and percentage of Hispanic students enrolled. This study analyzed student overall performance, mathematics performance, and English language arts (ELA) performance at the ROS utilizing three models: the unconditional model, the control model with student-level variables, and the full model with school-level variables. A fourth model was applied to a subset of the data for each academic area and included students' reason for choosing online coursework at level 1. The results identified multiple significant factors that predicted student performance. At the student level for all three academic areas, prior academic performance (GPA) was a positive predictor of student achievement while special education status and qualification for free or reduced-price lunch were negative predictors. At the school level, the only significant predictor is the average SAT score which positively predicts overall academic achievement at the ROS. When the students' reasons for selecting online coursework were analyzed, health reasons were a significant negative predictor for overall academic performance. Behavioral reasons were a significant positive predictor and family reasons were significant negative predictor of mathematics achievement at the ROS. The findings on significant predictors of student success in online classes are important information for students, parents, educators, and others. These findings can provide clarity in decision making around the placement and support of students. They also provide important areas of focus for program quality and improvement to support student success. Future research could investigate further the relationship between special education classifications, other school level factors, and additional reasons for selecting online courses, on the one hand, and success in on-line classes, on the other.

Using Learning Analytics to Predict Academic Success in Online and Face-to-face Learning Environments
Using Learning Analytics to Predict Academic Success in Online and Face-to-face Learning Environments

Author: Lisa Janine Berry

Publisher:

Published: 2017

Total Pages: 119

ISBN-13:

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"This learning analytics study looked at the various student characteristics of all on-campus students who were enrolled in 100 and 200 level courses that were offered in both online and face-to-face formats during a two-year period. There is a perception that online education is either not as successful as face-to-face instruction, or it is more difficult for students. The results of this study show this is not the case. The goal of this study was to complete an in-depth analysis of student profiles addressing a variety of demographic categories as well as several academic and course related variables to reveal any patterns for student success in either online or face-to-face courses as measured by final grade. There were large enough differences within different demographic and academic categories to be considered significant for the study population, but overwhelmingly, the most significant predictor of success was found to be past educational success, as reflected in a student's cumulative grade point average. Further analysis was completed on students who declared high school credit as their primary major based on significantly different levels of success. These students were concurrent enrollment students or those who completed college courses for both high school and university credit. Since most of these students were new to the university, they did not have a cumulative GPA, so other predictive factors were explored. The study concludes with recommendations for action based on the logistic regression prediction tool that resulted from the data analysis."--Boise State University ScholarWorks.

Predicting Student Success in a Self-paced Mathematics MOOC
Predicting Student Success in a Self-paced Mathematics MOOC

Author: James Allan Cunningham

Publisher:

Published: 2017

Total Pages: 115

ISBN-13:

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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.

Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-efficacy, and Hope
Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-efficacy, and Hope

Author: Reece E. Newman

Publisher:

Published: 2021

Total Pages: 613

ISBN-13:

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Abstract Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-Efficacy, and Hope Name: Newman, Reece Elton University of Dayton Advisor: Dr. Charles J. Russo This study of a convenience sample of 66 Introductory Computer Programming students at an urban Midwestern community college used age, computer programming experience, self-efficacy, and hope to predict overall course score. The age, computer programming experience, self-efficacy, and hope frequency distributions were not statistically normal or Gaussian in the sample. Computer programming experience statistically significantly correlated with both computer programming self-efficacy and computer programing hope. Age and computer programming experience, age and computer programming self-efficacy, and age and computer programming hope did not statistically significantly correlate. Computer programming self-efficacy and computer programming hope did not statistically significantly correlate. Relations between age and overall course score, computer programming experience and overall course score, computer programming self-efficacy and overall course score, and computer programming hope and overall course score were nonlinear, so the assumptions for correlation, simple linear regression, and hierarchical multiple linear regression did not hold for the sample data. Correlational, simple regression, and multiple hierarchical regression results were not statistically significant, nor were Student's independent samples t-tests, one-way ANOVAs, and twoway 2 X 2 and 3 X 2 ANOVAs. Despite the overall lack of statistical significance in the findings, there were novel contributions to human knowledge discovered through the observational study of the sample data. Instrument response patterns were internally consistent, providing evidence that the instruments are reliable in the introductory computer programming community college student sample. There were clustering and clear trends in the data indicating a broad range of responses to each instrument. The highly heterogeneous community college population was quite clearly distinct and different from much more homogeneous four-year college and university student populations.

Strengths-based Analysis of Student Success in Online Courses
Strengths-based Analysis of Student Success in Online Courses

Author: Carol Gering

Publisher:

Published: 2017

Total Pages: 394

ISBN-13:

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The purpose of this research was to increase understanding of post-secondary student success in online courses by evaluating a contextually rich combination of personal, circumstantial, and course variables. A strengths-based perspective framed the investigation. Mixed-method data were collected and analyzed sequentially in three phases: two phases of quantitative collection and analysis were followed by qualitative interviews and comprehensive analysis. The study first used logistic regression to analyze existing data on more than 27,000 student enrollments, spanning a time period of four academic years. The second phase of research enhanced the modeling focused on a subset of the total population; students from a single semester were invited to complete an assessment of non-cognitive attributes and personal perceptions. Between the two phases, 28 discreet variables were analyzed. Results suggest that different combinations of variables may be effective in predicting success among students with varying levels of educational experience. This research produced preliminary predictive models for student success at each level of class standing. The study concluded with qualitative interviews designed to explain quantitative results more fully. Aligned with a strengths-based perspective, 12 successful students were asked to elaborate on factors impacting their success. Themes that emerged from the interviews were congruent with quantitative findings, providing practical examples of student and instructor actions that contribute to online student success.

STEM Road Map
STEM Road Map

Author: Carla C. Johnson

Publisher: Routledge

Published: 2015-07-03

Total Pages: 374

ISBN-13: 1317620208

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STEM Road Map: A Framework for Integrated STEM Education is the first resource to offer an integrated STEM curricula encompassing the entire K-12 spectrum, with complete grade-level learning based on a spiraled approach to building conceptual understanding. A team of over thirty STEM education professionals from across the U.S. collaborated on the important work of mapping out the Common Core standards in mathematics and English/language arts, the Next Generation Science Standards performance expectations, and the Framework for 21st Century Learning into a coordinated, integrated, STEM education curriculum map. The book is structured in three main parts—Conceptualizing STEM, STEM Curriculum Maps, and Building Capacity for STEM—designed to build common understandings of integrated STEM, provide rich curriculum maps for implementing integrated STEM at the classroom level, and supports to enable systemic transformation to an integrated STEM approach. The STEM Road Map places the power into educators’ hands to implement integrated STEM learning within their classrooms without the need for extensive resources, making it a reality for all students.