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

By: Goran Trajkovski (Editor), Marylee Demeter (Editor), Heather Hayes (Editor)

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Hardcover | 6 May 2022

At a Glance

Hardcover
344 Pages

Dimensions(cm)
27.94 x 21.59 x 2.06

Hardcover

$384.04

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This publication examines novel and emerging applications of data science and sister disciplines in gaining insights from data to inform interventions into the learners' journey and interactions with an academic or training institution. Topics focus on building models of learners for success, using data to inform courseware and assessmentware development, and planning services supporting the learning process, including capturing, understanding, impacting, and implementing changes in learning, teaching, and assessment. Data are collected at various times and places throughout the learners' lifecycles, and the learners and the institution should benefit from the insights and knowledge gained from those data.

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. Various experiments have been piloted across learning and training institutions with various degrees of success limited largely to the interest of individuals or small groups in affecting academic and business operations supporting learning activities. As many training and academic institutions are maturing in their data-driven decisioning, useful, scalable, and interesting trends may start emerging, and organizations can benefit from sharing information on those efforts. While training and academic institutions may vary in definition, approach, size, and mission, learning about the learner and providing services that are as closely aligned to their behaviors as needs is the essence of their existence.

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