Online learning has increasingly been viewed as a possible way to remove barriers associated with traditional face-to-face teaching, such as overcrowded classrooms and shortage of certified teachers. While online learning has been recognized as a possible approach to deliver more desirable learning outcomes, close to half of online students drop out as a result of student-related, course-related, and out-of-school-related factors (e.g., poor self-regulation; ineffective teacher-student, student-student, and platform-student interactions; low household income). Many educators have expressed concern over students who unexpectedly begin to struggle and appear to fall off track without apparent reason. A well-implemented early warning system, therefore, can help educators identify students at risk of dropping out and assign and monitor interventions to keep them on track for graduation. Despite the popularity of early warning systems, research on their design and implementation is sparse. Early Warning Systems and Targeted Interventions for Student Success in Online Courses is a cutting-edge research publication that examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of early warning systems and targeted interventions and discusses their implications for policy and practice. Moreover, this book will review common challenges of early warning systems and dashboard design and will explore design principles and data visualization tools to make data more understandable and, therefore, more actionable. Highlighting a range of topics such as curriculum design, game-based learning, and learning support, it is ideal for academicians, policymakers, administrators, researchers, education professionals, instructional designers, data analysts, and students.

Students who self-regulate are more likely to improve their academic performance, find value in their learning process, and continue to be effective lifelong learners. However, online students often struggle to self-regulate, which may contribute to lower academic performance. Likewise, less experienced online teachers who are in the process of implementing—or have implemented—a shift from in-person to distance learning may struggle to enable their students to employ effective self-regulation techniques. Supporting Self-Regulated Learning and Student Success in Online Courses examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of self-regulated learning models and interventions in online courses and discusses their implications. Covering key topics such as online course design, student retention, and learning support, this reference work is ideal for administrators, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

Students who self-regulate are more likely to improve their academic performance, find value in their learning process, and continue to be effective lifelong learners. However, online students often struggle to self-regulate, which may contribute to lower academic performance. Likewise, less experienced online teachers who are in the process of implementing—or have implemented—a shift from in-person to distance learning may struggle to enable their students to employ effective self-regulation techniques. Supporting Self-Regulated Learning and Student Success in Online Courses examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of self-regulated learning models and interventions in online courses and discusses their implications. Covering key topics such as online course design, student retention, and learning support, this reference work is ideal for administrators, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

Online learning has increasingly been viewed as a possible way to remove barriers associated with traditional face-to-face teaching, such as overcrowded classrooms and shortage of certified teachers. While online learning has been recognized as a possible approach to deliver more desirable learning outcomes, close to half of online students drop out as a result of student-related, course-related, and out-of-school-related factors (e.g., poor self-regulation; ineffective teacher-student, student-student, and platform-student interactions; low household income). Many educators have expressed concern over students who unexpectedly begin to struggle and appear to fall off track without apparent reason. A well-implemented early warning system, therefore, can help educators identify students at risk of dropping out and assign and monitor interventions to keep them on track for graduation. Despite the popularity of early warning systems, research on their design and implementation is sparse. Early Warning Systems and Targeted Interventions for Student Success in Online Courses is a cutting-edge research publication that examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of early warning systems and targeted interventions and discusses their implications for policy and practice. Moreover, this book will review common challenges of early warning systems and dashboard design and will explore design principles and data visualization tools to make data more understandable and, therefore, more actionable. Highlighting a range of topics such as curriculum design, game-based learning, and learning support, it is ideal for academicians, policymakers, administrators, researchers, education professionals, instructional designers, data analysts, and students.