The study gathered data on learning styles, technical comfort level, age, gender, and undergraduate or graduate status to determine if these are predictors of student satisfaction in online learning environments. Student satisfaction was considered in terms of learner-content interaction, learner-instructor interaction, learner-learner interaction, learner-technology interaction, and general satisfaction. Based on the multiple regression analysis, several variables were identified which are predictors of student satisfaction in online learning environments. Age and level of technical comfort were significant predictors of Learner-Content Interaction Satisfaction, Learner-Instructor Interaction Satisfaction, and General Satisfaction scores. None of the named variables was a significant predictor of Learner-Learner Interaction Satisfaction scores. Sensing or intuitive learning style and level of technical comfort were significant predictors of Learner-Technology Interaction Satisfaction scores. The conclusions may help guide course designers and instructors in designing and delivering online instruction that is satisfying to learners and may inform institutions as they strive to attract and retain students. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]