Exponential increases in the number of learners receiving instruction through online learning, and institutions using various online modalities for instructional delivery, make salient the need for distance learning policies in higher education, as well as K-12 domains, that focus beyond teacher presence (or student/learner-instructor) and learner-learner (student-student) interactions to other forms of interactions; most notably student interactions or engagement with online course contents (that is, learner-content (L-C) interactions). Most distance learning policies do not sufficiently address the impact of learner-content (L-C) interaction in learning environments even though a substantial proportion of students' interactions are spent on course contents. For example, in New York State, Commissioner's Regulation Section 50.1 (o) stipulates that for every "45 hours (one semester credit) of learning time," 30 hours (70%) should be spent on student work/study out of class, that is, "course contents, which" includes: "reading course presentations/'lectures,' reading other materials, participation in online discussions, doing research, writing papers or other assignments, and completing all other assignments (e.g. projects)." Understanding the impact of L-C Interaction would help decrease perceived distance and increase psychological closeness between learners and instructors and among learners in online learning environments. To bridge this gap, this study captured various patterns of learner-content interactions (a form of dialogue outlined in Moore (1993) Transactional Distance Theory) in several graduate-level online courses offered by The School of Professional Development (SPD) between 2013 and 2016. The sample comprised of N = 631 students from 42 asynchronous online courses offered between 2013 and 2016. Some of the courses had more than one section, and most courses were offered in more than one semester. Learner analytics for learner-content interactions included Time Spent on Content, Frequency of Access to Content, access to lecture capture and archived lectures, time spent viewing archived lectures, frequency of written discussions, discussion board and other media (such as web conferencing and collaboration tools) posts, and frequency of posts (written) with academic content (that is, reflective, socratic, and informative). Some of the findings indicate that most SPD students, based on the specific courses analyzed in this study, are engaged in the course on Wednesdays between 7p.m. and 9p.m; are more engaged in the content materials and assignment folders than with other folders inside the content area; and are more engaged, as expected, in the discussion board message forums (especially during week 1 and weeks 9 and 10) than in any other content area applications, second of which is the announcement content area application. Further analysis of these patterns of learner analytics showed that final paper, and not posts on discussion board forum, was a better predictor of final grade in the course. Predictive modeling was then attempted to develop a robust Learner-Content interaction model that would optimize specific learner outcomes, such as student assessment scores and final course grades. Policies are proposed for analyzing the implications of learner-content interactions on student outcomes and perceived distance; and the use of learner analytics in online learning environments. Policies are also proposed for ensuring quality assurance mechanisms in this environment. [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.]