The Future of Education

Edition 13

Accepted Abstracts

Online Group Embedded Figures Test and Student’s Success in Online Course

Wook-Sung Yoo, Department of Computer Science and Software Engineering School of Engineering Fairfield University (United States)

Sahng-Ah Yoo, Department of Psychology Columbia University (United States)


A growing number of students are enrolling in distance learning online college degree programs these days and MOOC (Massive Open Online Course), an online course aimed at unlimited participation, enables free university-level education on an enormous scale. Despite many exciting developments and applications of online courses, however, online education is not for all students: MOOCs classes have average completion rates of less than 13% and recent study of Washington State community college students showed that the students who took online courses were more likely to fail or drop out of the course than students who took the same course in person. It is partly because an online learning style is considerably different from a traditional class-based learning style and many students fail to adapt the online learning style as each individual perceives, interacts with and responds to the learning environment differently. Given the growing popularity and benefits of online learning, improving the success rates of online learners will be extremely beneficial to each individual student and educational institution.

In this paper, we examine the possible correlation between individual’s cognitive perceptual ability and the success rate in online course by using Group Embedded Figures Test (GEFT) as a research tool. GEFT is a perceptual test originally developed to test a subject’s cognitive ability to locate a simple shape embedded within a complex figure. GEFT is also used as an assessment tool for other purpose exploring analytical ability, social behavior, and problem solving style.

To conduct the research, we first redesigned Online Group Embedded Figures Test (OGEFT), a web based testing tool to administer the Group Embedded Figures Test (GEFT) over long distances. OGEFT, initially developed in 2006 using Java Applet, was newly designed using the latest web technology (html5, CSS, jQuery) in 2014 to resolve the browser compatibility issue and adding an auto-grading feature to conduct the research for widespread geographic population at the minimum efforts. 75 students at the Fairfield University participated the experiment and our preliminary experimental results showed a meaningful relationship between the score of GEFT and the success rate of the online course of individual student. We plan to have extensive research to a larger populations to verify the results to support the idea of using GEFT score to advise students properly before they are taking online course. Statistical analysis and future plan will be also discussed.

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