Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan

Description

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians. Cover Title Page Copyright Page Book Series Table of Contents Detailed Table of Contents Preface Section 1: Perspectives on Data Chapter 1: Mind the Gap Chapter 2: Relationships Between Out-of-School-Time Lessons and Academic Performance Among Adolescents in Four High-Performing Education Systems Chapter 3: An Assessment of Self-Service Business Intelligence Tools for Students Section 2: Applications Chapter 4: Univariate and Multivariate Filtering Techniques for Feature Selection and Their Applications in Field of Machine Learning Chapter 5: Scalable Personalization for Student Success Chapter 6: User Sentiment Analysis and Review Rating Prediction for the Blended Learning Platform App Chapter 7: Analyzing and Predicting Learner Sentiment Toward Specialty Schools Using Machine Learning Techniques Section 3: Advancing the Profession Chapter 8: Working Towards a Data Science Associates Degree Program Chapter 9: A Storied Approach to Learning Data Analytics in a Graduate Data Analytics Program Chapter 10: Identifying Structure in Program-Level Competencies and Skills Section 4: Considerations Chapter 11: Don't @ Me Chapter 12: The Secret Lives of ePortfolios Chapter 13: Do We Need a Digital Data Exorcism? Compilation of References About the Contributors Index