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.
"This book is a game changer! Strengths-Based Teaching and Learning in Mathematics: 5 Teaching Turnarounds for Grades K- 6 goes beyond simply providing information by sharing a pathway for changing practice. . . Focusing on our students’ strengths should be routine and can be lost in the day-to-day teaching demands. A teacher using these approaches can change the trajectory of students’ lives forever. All teachers need this resource! Connie S. Schrock Emporia State University National Council of Supervisors of Mathematics President, 2017-2019 NEW COVID RESOURCES ADDED: A Parent’s Toolkit to Strengths-Based Learning in Math is now available on the book’s companion website to support families engaged in math learning at home. This toolkit provides a variety of home-based activities and games for families to engage in together. Your game plan for unlocking mathematics by focusing on students’ strengths. We often evaluate student thinking and their work from a deficit point of view, particularly in mathematics, where many teachers have been taught that their role is to diagnose and eradicate students’ misconceptions. But what if instead of focusing on what students don’t know or haven’t mastered, we identify their mathematical strengths and build next instructional steps on students’ points of power? Beth McCord Kobett and Karen S. Karp answer this question and others by highlighting five key teaching turnarounds for improving students’ mathematics learning: identify teaching strengths, discover and leverage students’ strengths, design instruction from a strengths-based perspective, help students identify their points of power, and promote strengths in the school community and at home. Each chapter provides opportunities to stop and consider current practice, reflect, and transfer practice while also sharing · Downloadable resources, activities, and tools · Examples of student work within Grades K–6 · Real teachers’ notes and reflections for discussion It’s time to turn around our approach to mathematics instruction, end deficit thinking, and nurture each student’s mathematical strengths by emphasizing what makes them each unique and powerful.
This book outlines a comprehensive, collaborative approach to teaching students living with trauma, violence, and chronic stress that focuses on students' strengths and resiliency.
"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.
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.
Distance Learning is for leaders, practitioners, and decision makers in the fields of distance learning, e-learning, telecommunications, and related areas. It is a professional journal with applicable information for those involved with providing instruction to all kinds of learners, of all ages, using telecommunications technologies of all types. Stories are written by practitioners for practitioners with the intent of providing usable information and ideas. Articles are accepted from authors--new and experienced--with interesting and important information about the effective practice of distance teaching and learning. Distance Learning is published quarterly. Each issue includes eight to ten articles and three to four columns, including the highly regarded "And Finally..." column covering recent important issues in the field and written by Distance Learning editor, Michael Simonson. Articles are written by practitioners from various countries and locations, nationally and internationally.
Students who self-regulate are more likely to improve their academic performance, find value in their learning process, and continue to be effective lifelong learners. However, online students often struggle to self-regulate, which may contribute to lower academic performance. Likewise, less experienced online teachers who are in the process of implementing—or have implemented—a shift from in-person to distance learning may struggle to enable their students to employ effective self-regulation techniques. Supporting Self-Regulated Learning and Student Success in Online Courses examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of self-regulated learning models and interventions in online courses and discusses their implications. Covering key topics such as online course design, student retention, and learning support, this reference work is ideal for administrators, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.
Helps aspiring college students discover where their strengths truly lie and how to develop them to reach their full potential at school and later in the real world.
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Higher Education Learning Methodologies and Technologies Online, HELMeTO 2019, held in Novedrate, Italy, in June 2019. The 15 revised full papers and 2 short papers presented were carefully reviewed and selected from a total of 39 submissions. The papers are organized in topical sections on online pedagogy and learning methodologies; learning technologies, data analytics and educational big data mining as well as their applications; the challenge of online sport and exercise sciences university programs.
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.
