Actionability is a critical issue in learning analytics for driving impact in learning, bridging the gap between insights and improvement. This dissertation places actionability at the forefront, integrating it throughout the learning analytics process to fully leverage its potential. The study involves designing, developing, and implementing student-facing analytics for promoting actionability within the context of collaborative annotation. The design work (Chapter 4) used a human-centered process involving students as co-designers and instructors as reviewers. Six design strategies for actionability emerged, each of which informed subsequent design activities: (1) don't let data limit early ideas; (2) think in the first person; (3) bring in temporality; (4) consider a plurality of solutions; (5) situate analytics into students' routines; and (6) imagine unintended, undesirable actions. During this process, the main challenge in collaborative annotation was identified as finding meaningful places to contribute, and various ideas for the analytic tool were generated to address this need. The development work (Chapter 5) focused on creating the analytics designed for promoting actionability. The design concept produced by the human-centered process in Chapter 4 was translated into the product's details and features. Seven distinct features of actionable analytics were identified: (1) prospective suggestions individualized to students; (2) integration into existing tools rather than standalone tools; (3) timing of analytics that can match timing of learning; (4) limited quantity of information provided at one time; (5) information presented in an accessible format; (6) direct paths to actions; (7) customizability for agency. Actionability as a criterion guided the development of the specific metrics for the collaborative annotation context, resulting in five types of suggestions for where students could contribute to collaborative annotation tasks. The implementation work (Chapter 6) provided the developed analytics to 91 students in a live course over five weeks. Findings showed that while opening the analytics promptly, students used the analytics in different ways, either backward or forward in their learning routines. They rarely took immediate action based on the analytics, instead making indirect changes in their reading, commenting, and revisiting behaviors in collaborative annotation. This highlights the multifaceted nature of analytic actionability and the need for enhanced support in translating insights to actions. Using an open-box design narrative, this study offers in-depth descriptions of developing tools for human-centered design processes, characteristics of what analytics designed for actionability look like, and ways to examine their direct and indirect roles in student learning. The study serves as a starting point to explore the potential of meaningful ways to close-the-loop in learning analytics by investigating how students can incorporate analytics-informed insights and actions into their learning tasks. [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.]