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.
General
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