The Open Learning Initiative:<br>Measuring the Effectiveness of the OLI Statistics Course in<br>Accelerating Student Learning


Authors: 
Marsha Lovett, Oded Meyer &amp; Candace Thille
Type: 
Pages: 
online
Year: 
2008
Publisher: 
Carnegie Mellon University
URL: 
http://www.cmu.edu/oli/publications/LovettMeyerThille-OpenLearn.pdf
Abstract: 

The Open Learning Initiative (OLI) is an open educational resources project at<br>Carnegie Mellon University that began in 2002 with a grant from The William and Flora Hewlett<br>Foundation. OLI creates web-based courses that are designed so that students can learn<br>effectively without an instructor. In addition, the courses are often used by instructors to support<br>and complement face-to-face classroom instruction. Our evaluation efforts have investigated OLI<br>courses' effectiveness in both of these instructional modes - stand-alone and hybrid.<br>This report documents several learning effectiveness studies that were focused on the OLIStatistics<br>course and conducted during Fall 2005, Spring 2006, and Spring 2007. During the Fall<br>2005 and Spring 2006 studies, we collected empirical data about the instructional effectiveness of<br>the OLI-Statistics course in stand-alone mode, as compared to traditional instruction. In both of<br>these studies, in-class exam scores showed no significant difference between students in the<br>stand-alone OLI-Statistics course and students in the traditional instructor-led course. In contrast,<br>during the Spring 2007 study, we explored an accelerated learning hypothesis, namely, that<br>learners using the OLI course in hybrid mode will learn the same amount of material in a<br>significantly shorter period of time with equal learning gains, as compared to students in<br>traditional instruction. In this study, results showed that OLI-Statistics students learned a full<br>semester's worth of material in half as much time and performed as well or better than students<br>learning from traditional instruction over a full semester.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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