Factors Encouraging or Discouraging Students from Taking Online Classes

2010 ◽  
pp. 55-67 ◽  
Author(s):  
Chuleeporn Changchit ◽  
Tim Klaus
Keyword(s):  
Student Retention ◽  
Online Courses ◽  
Communication Technologies ◽  
Retention Rates ◽  
Online Classes ◽  
Continuing Higher Education ◽  
Online Programs ◽  
Individual Variations ◽  
Traditional Courses ◽  
Use Of The Internet

Advances in communication technologies, such as widespread use of the Internet, have opened new avenues for continuing higher education. These advances have allowed educators to provide for and satisfy individual variations in learning. Generally, online courses are adaptations of traditional courses; some courses are more suitable than others for such online instruction. As the trend to offer online courses continues, understanding the factors that lead to students’ preference can be useful. Online courses can be costly to develop and to implement, and inappropriately designating courses for online participation can lead to lower student retention rates. This study focuses on students’ perceptions of online courses. The results identify issues that affect students’ perceptions, and this study concludes by suggesting ways for universities to design online programs that better suit the desires of students.

Classroom Preferences

2010 ◽  
pp. 1268-1279
Author(s):  
Chuleeporn Changchit ◽  
Tim Klaus
Keyword(s):  
Student Retention ◽  
Online Instruction ◽  
Online Courses ◽  
Retention Rates ◽  
Online Course ◽  
Online Programs ◽  
Traditional Courses ◽  
Course Offerings

Advances in technology have enabled instructors to design online courses that better meet the needs of students. Online courses generally are adaptations of traditional courses; some courses are more suitable for such online instruction. As the trend of online course offerings continues, universities must understand factors that lead to students’ preferences since online courses can be costly to develop and implement and inappropriate online coursescan lead to lower student retention rates. This study focuses on students’ perceptions of online courses. The results identify issues that affect students’ perceptions and this study concludes by suggesting ways for universities to design online programs that better suit the desires of students.

Designing Online Courses to Promote Student Retention

2007 ◽  
Vol 36 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Beth Dietz-Uhler ◽  
Amy Fisher ◽  
Andrea Han
Keyword(s):  
Student Retention ◽  
Course Design ◽  
Online Courses ◽  
Retention Rate ◽  
Retention Rates ◽  
Online Course ◽  
Quality Matters ◽  
Face To Face ◽  
Online Distance Learning ◽  
Online Distance

Although the issue of student retention is a campus-wide one, it is of special interest in online distance learning courses, where retention rates are reported to be lower than in face-to-face classes. Among the explanations and theories of retention rates in online courses, one that struck us as most useful is a structural one, namely, course design. The question we raise is, can online course designs promote student retention? In this article, we report on how we used Quality Matters to design and revise online courses in psychology and statistics. Quality Matters, a research-based initiative, advocates the use of eight general review standards to review online courses. In our psychology and statistics courses, our retention rate across multiple offerings of both courses is approximately 95%.

scholarly journals Learners’ Perceptions towards Online Learning during the Covid-19 Pandemic: A Study at a Center of Foreign Languages in Vietnam

2021 ◽  
Vol 5 (3) ◽  
pp. 84-90
Author(s):  
Hung Nguyen Quoc Luu
Keyword(s):  
Online Learning ◽  
Online Education ◽  
Online Courses ◽  
Retention Rates ◽  
Foreign Languages ◽  
Traditional Learning ◽  
Online Learners ◽  
Online Programs ◽  
Face To Face ◽  
Face To Face Learning

Online learning is a revolutionary trend of educational technology today. Particularly, online learning has become the donminent alternative to traditional learning during the Covid-19 pandemic and in the new normal. Various studies have shown that online learning has varying effectiveness at all levels and types of education; however, it is worth noting that many learners are still struggling to accept the mode of online learning, and learner retention has been suggested as one of the greatest weaknesses in online education. The current study used a questionnaire with open-ended questions to examine online learners’ perceptions of online learning as well as to understand reasons for their retention in online courses. The results revealed that most of the learners, especially young learners still preferred traditional face-to-face learning to online learning due to various problems they experienced while doing online learning, such as lack of facilities and necessary interactions. However, the retention rates are relatively modest, indicating the perceived quality of online programs at a center of foreign languages in Vietnam.

scholarly journals An examination of factors that impact the retention of online students at a for-profit university

2017 ◽  
Vol 21 (3) ◽  
Author(s):  
Chris Sorensen ◽  
Judy Donovan
Keyword(s):  
Undergraduate Students ◽  
Online Survey ◽  
Online Courses ◽  
Qualitative Content Analysis ◽  
Drop Out ◽  
Retention Rates ◽  
Online Programs ◽  
For Profit ◽  
Survey Interviews ◽  
The University

Online learning has continued to grow in recent years. However, retaining students in online courses and programs has posed a challenge. Whether the university is public, private, offers both face-to-face and online programs, or is 100% online, retaining students in online programs can be an issue. This study reflects the widespread desire at a large online for-profit university to improve student retention rates. The goal of the research was to provide further insight into why students may decide to drop out of online programs. Participants consisted of former undergraduate students at the university in the College of Education who dropped out without providing a specific reason for doing so. The study used a non-experimental mixed methods approach collecting data from university databases, an online survey, interviews, and classroom walk-throughs. Data analysis employed techniques such as frequency calculations, a MANOVA, and qualitative content analysis. Results from the MANOVA revealed statistically significant results when examining student GPA and last course grade. Furthermore, data collected from the online survey, interviews, and classroom walk-throughs revealed common reasons for why students may drop out of online programs.

scholarly journals DEVELOPING AND IMPLEMENTING COMPREHENSIVE STUDENT SUPPORT SERVICES FOR ONLINE STUDENTS

2013 ◽  
Vol 17 (1) ◽  
Author(s):  
Marwin Britto ◽  
Susan Rush
Keyword(s):  
Student Retention ◽  
Support Services ◽  
Student Support Services ◽  
Online Courses ◽  
Student Support ◽  
Retention Rates ◽  
Online Students ◽  
Attrition Rates ◽  
Online Student ◽  
Successes And Challenges

The issue of “student retention” in higher education has become a critical focus in recent years. This problem is even more severe with students enrolled in online courses. While many institutions have tried to address this persistent issue, few have been able to mount an effective and sustained strategy to decrease attrition rates, particularly for students in online courses. This paper describes the efforts of Lone Star College-Online and its Online Student Support Services unit to build and implement a comprehensive suite of services for online students with the intention of enhancing the online student experience and ultimately, directly and indirectly, improving student retention rates. It is hoped that others can learn and gain from the shared successes and challenges presented in this paper.

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

2018 ◽  
Vol 7 (2.29) ◽  
pp. 1113
Author(s):  
Nadirah Mohamad ◽  
Nor Bahiah Ahmad ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Data Mining ◽  
Student Retention ◽  
Online Courses ◽  
Predictive Analytics ◽  
Promising Result ◽  
Intervention Programs ◽  
Retention Rates ◽  
Learning Program ◽  
Interaction Factor ◽  
Using Data

Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.

scholarly journals Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

2018 ◽  
Vol 7 (2.29) ◽  
pp. 398 ◽  
Author(s):  
Nadirah Mohamad ◽  
Nor Bahiah Ahmad ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Data Mining ◽  
Student Retention ◽  
Online Courses ◽  
Predictive Analytics ◽  
Promising Result ◽  
Intervention Programs ◽  
Retention Rates ◽  
Learning Program ◽  
Interaction Factor ◽  
Using Data

Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.  

Collaborative Learning in the Speech-Language Pathology and Audiology Classroom

2008 ◽  
Vol 11 (1) ◽  
pp. 9-11 ◽  
Author(s):  
Carolyn S. Potts ◽  
Sarah M. Ginsberg
Keyword(s):  
Collaborative Learning ◽  
Student Retention ◽  
Student Learning ◽  
Student Learning Outcomes ◽  
Retention Rates ◽  
Speech Language Pathology ◽  
Traditional Lecture ◽  
Language Pathology ◽  
Improve Student Learning

Abstract In recent years, colleges and universities across the country have been called upon to increase the quality of education provided and to improve student retention rates. In response to this challenge, many faculty are exploring alternatives to the traditional “lecture-centered” approach of higher education in an attempt to increase student learning and satisfaction. Collaborative learning is one method of teaching, which has been demonstrated to improve student learning outcomes.

Increasing undergraduate student retention rates

1995 ◽  
Vol 23 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Barbara Rickinson ◽  
Desmond Rutherford
Keyword(s):  
Student Retention ◽  
Undergraduate Student ◽  
Retention Rates
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