Applying Data Science and Learning Analytics Throughout a Learner's Lifespan
- 7h 59m
- Goran Trajkovski, Heather Hayes, Marylee Demeter
- IGI Global
- 2022
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.
About the Author
Goran Trajkovski Ph.D., MSML, MSCS, MBA, currently serves as a Senior Lead College Compliance Advisor at Western Governors University. He specializes in program, courseware, and assessmentware design and development for high-demand computing and data-science disciplines, and in the regulatory and accreditation processes in the higher education domain. He has been involved in the eLearning movement since its inception, through the conceptualization, development, and deployment of more than 35 undergraduate and graduate programs in a variety of disciplines for online delivery and leading the development of more than 300 online courses. During his three decades in Higher Education, he has served in a variety of instructional and administration roles, including Dean of the School of Computer Information Systems at Virginia International University, Executive Director of Adult and Graduate Studies at Marian University, and Director of Product Strategy and Development at Laureate Education, Inc.
Marylee Demeter possesses administrative and academic experience in higher education serving in assessment leadership and adjunct positions at Rutgers University and Middlesex County College. She championed efforts as Chair of the Professional Development Committee for the Student Affairs Assessment Leaders, where she collaboratively developed the MOOC “Developing and Leading Assessment in Student Affairs.” She currently serves as a Senior Assessment Developer at Western Governors University.
Heather Hayes, PhD, is a psychometrician for the Colleges of Information Technology and Business at Western Governors University. She has been involved in the construction and validation of both cognitive ability and personality assessments for over 20 years. Her research interests center on the conjoint use of cognitive theory and Item Response Theory to aid in the construct validation of test scores as well as to improve the test experience itself through computer adaptive testing and automatic item generation. Her current focus is application of this line of research to developing and validating educational tests for online, competency-based education programs.
In this Book
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Mind the Gap—From Typical LMS Traces to Learning to Learn Journeys
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Relationships between Out-of-School-Time Lessons and Academic Performance among Adolescents in Four High-Performing Education Systems
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An Assessment of Self-Service Business Intelligence Tools for Students—The Impact of Cognitive Needs and Innovative Cognitive Styles
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Univariate and Multivariate Filtering Techniques for Feature Selection and Their Applications in Field of Machine Learning
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Scalable Personalization for Student Success—A Framework for Using Machine Learning Methods in Self-Directed Online Courses
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User Sentiment Analysis and Review Rating Prediction for the Blended Learning Platform App
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Analyzing and Predicting Learner Sentiment toward Specialty Schools Using Machine Learning Techniques
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Working towards a Data Science Associates Degree Program—Impacts, Challenges, and Future Directions
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A Storied Approach to Learning Data Analytics in a Graduate Data Analytics Program
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Identifying Structure in Program-Level Competencies and Skills—Dimensionality Analysis of Performance Assessment Scores from Multiple Courses in an IT Program
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Don't @ Me—A Study of the Perception of Twitter Users of Educational Offerings
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The Secret Lives of ePortfolios—Text Network Analysis and the Future of Algorithmic Hiring
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Do We Need a Digital Data Exorcism? End of Life Considerations of Data Mining Educational Content
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Compilation of References
