Despite increasing enrollment percentages from earlier years, online courses continue to show receding student retention rates. To reduce attrition and ensure continual growth in online courses, it is important to continue to review current and updated literature to understand the changing behaviors of online learners and faculty in the 21st century and examine how they fit together as a cohesive educational unit. This article reviews literature to ascertain critical reasons for high attrition rates in online classes, as well as explore solutions to boost retention rates. This will help create a starting point and foundation for a more, in-depth research and analysis of retention issues in online courses. Examining these issues is critical to contemporary learning environments.

/pdf/retention-in-online-courses-exploring-issues-and-solutions-a-19m3aopfoo.pdf

Retention in Online Courses: Exploring Issues and Solutions—A Literature Review

Eighty-four students in an online health education doctoral program taking the first course in the program over one year (four quarters) were surveyed in regards to their computer, Internet, and online course anxiety, and overall course satisfaction. An 18-item anxiety tool with domains in computer, Internet, and online learning was administered in the first and last weeks of an educational research course to assess for changes in student anxiety. A 24-item satisfaction tool with domains regarding the instructor, technology, setup, interaction, outcomes, and overall satisfaction was administered at the end of the course. Results show a significant negative correlation between anxiety and student satisfaction. Student anxiety levels were in the relatively moderate range; changes in anxiety levels over time were not significant. Participants who felt anxious when using computers or the Internet, or when taking online courses experienced anxiety with other domains.

/pdf/student-perceptions-of-satisfaction-and-anxiety-in-an-online-1oqtova7le.pdf

Student Perceptions of Satisfaction and Anxiety in an Online Doctoral Program

Although online courses at postsecondary institutions promise adults access, flexibility, and convenience, many barriers to online learning remain. This article presents findings from a qualitative case study, which explored the phenomenon of undergraduate and graduate women learners’ persistence in online degree-completion programs at a college in the Northeast of the United States. Research questions asked why women learners persisted or failed to persist, and how factors supporting or hindering persistence influenced learners. Interviews with a purposeful sample of 20 participants revealed the complexity of variables affecting learners’ persistence to graduation. Findings suggested that multiple responsibilities, insufficient interaction with faculty, technology, and coursework ranked highest as barriers to women’s persistence. Strong motivation to complete degrees, engagement in the learning community, and appreciation for the convenience of an online degree-completion option facilitated persistence.

http://www.irrodl.org/index.php/irrodl/article/download/455/1069

Persistence of Women in Online Degree-Completion Programs

International Journal of Self-Directed Learning

A plethora of research has been done in the past focusing on predicting student’s performance in order to support their development. Many institutions are focused on improving the performance and the education quality; and this can be achieved by utilizing data mining techniques to analyze and predict students’ performance and to determine possible factors that may affect their final marks. To address this issue, this work starts by thoroughly exploring and analyzing two different datasets at two separate stages of course delivery (20% and 50% respectively) using multiple graphical, statistical, and quantitative techniques. The feature analysis provides insights into the nature of the different features considered and helps in the choice of the machine learning algorithms and their parameters. Furthermore, this work proposes a systematic approach based on Gini index and p -value to select a suitable ensemble learner from a combination of six potential machine learning algorithms. Experimental results show that the proposed ensemble models achieve high accuracy and low false positive rate at all stages for both datasets.

pdf/systematic-ensemble-model-selection-approach-for-educational-1jo1uljld5.pdf

Systematic ensemble model selection approach for educational data mining

With the exponential growth of online courses in higher education, retention is an area of great concern. Online student retention has been suggested as one of the greatest weaknesses in online education (Carr, 2000; O'Brien, 2002). Studies show that the failed retention rate for online college and university undergraduates range from 20 to 50% and that online course administrators believe the failed retention rate for online courses to be 10 to 20% higher than traditional classroom environments (Frankola, 2001; Diaz, 2002). The number of college students who are participating in online courses continues to increase dramatically despite the greater likelihood of non-completion. Between 1995 and 1998 the number of institutions offering online courses essentially tripled and in the academic year 1999-2000 alone the number of students who took at least one online course increased by 57 percent ( National Center for Education Statistics, 2002).

/pdf/staying-the-course-a-study-in-online-student-satisfaction-2bj9po3o4f.pdf

Staying the Course: A Study in Online Student Satisfaction and Retention

Faculty Motivation for Open Educational Resources Textbook Selection and Future Use (Poster 14)

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Staying the Course: A Study in Online Student Satisfaction and Retention

Faculty Motivation for Open Educational Resources Textbook Selection and Future Use (Poster 14)