Utilizing Learning Analytics to Support Study Success

An edited volume by

Dirk Ifenthaler, Dana-Krisin Mah, and Jane Yin-Kim Yau

to be published by Springer, New York (www.springer.com/book/9783319647913)

Introduction

Advances in educational technology have enabled opportunities to provide insight into how learners engage within the learning environment provided. The resulting availability of vast amounts of educational data can represent how students interact with higher education resources and further analysis may provide useful insights into learning behavior and processes. From a holistic point of view, learning analytics use static and dynamic educational information from digital learning environments, administrative systems, and social platforms for real-time modelling, prediction, and optimization of learning processes, learning environments, and educational decision-making. Accordingly, learning analytics are expected to provide benefits for all stakeholders (i.e., students, teachers, designers, administrators, etc.) in the higher education arena.

In particular, students may benefit from learning analytics through personalised and adaptive support of their learning journey. For example, students often enter higher education academically unprepared and with unrealistic perceptions and expectations of academic competencies for their studies. Both, the inability to cope with academic requirements as well as unrealistic perceptions and expectations of university life, in particular with regard to academic competencies, are important factors for leaving the institution prior to degree completion. Still, research in learning analytics and how they support students at higher education institutions is scarce.

Coverage

The edited volume “Utilizing Learning Analytics to Support Study Success” aims to provide insight into how educational data and innovative digital technologies contribute toward successful learning and teaching scenarios. It features four major themes:

Part I. Theoretical perspectives linking learning analytics and study success

Part II. Technological innovations for supporting student learning

Part III. Issues and challenges for implementing learning analytics at higher education institutions

Part IV. Case studies showcasing successfully implemented learning analytics strategies at higher education institutions

Call for Proposals

Prospective authors (co-authors are welcome) are invited to submit a chapter proposal, including title, abstract (max. 300 words), five keywords, and the part of the book (see above) not later than 01 October 2017 to Dirk Ifenthaler (dirk@ifenthaler.info).

The proposal should be a previously unpublished work. Upon acceptance of the chapter proposal, the final chapter should be completed not later than 01 March 2018. Contributions will be blind reviewed and returned with comments by 01 April 2018. Finalised chapters are due no later than 01 May 2018. The final contributions should not exceed 20 manuscript pages. Guidelines for preparing chapters will be sent to authors upon acceptance of the proposal.

Timeline

The following represents a timeline for completing the edited volume:

  • 01 October 2017: Proposal due including title, abstract, keywords
  • 15 October 2017: Notification and additional information for accepted authors
  • 01 March 2018: Draft chapters due
  • 01 April 2018: Chapters returned with reviewers’ comments
  • 01 May 2018: Final chapters due

Inquiries and Submissions

Please forward your inquires and submissions to:

Professor Dirk Ifenthaler

Learning, Design and Technology

University of Mannheim

Email: dirk@ifenthaler.info

Web: www.ifenthaler.info

Twitter: @ifenthaler

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