An edited volume by
Dirk Ifenthaler (Curtin University, University of Mannheim) and David Gibson (Curtin University)
to be published bySpringer, New York
The UNESCO Chair for Data Science in Higher Education Learning and Teaching (https://research.curtin.edu.au/projects-expertise/institutes-centres/unesco/) aims to advance global knowledge, practice and policy in applying data science to transform higher education learning and teaching to improve personalisation, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive insights and recommendations. Although the field of learning analytics is receiving a lot of attention for its capacity to provide lead indicators of student failure and supporting learning processes, it has primarily focused to date on individual courses in isolation, rather than the capabilities of higher education institutions as learning organizations. Accordingly, the implementation of learning analytics at higher education institutions may have broad implications for the organisation (e.g., technological infrastructure, policies and regulations) and its stakeholders (e.g., students, academic staff, administrators) including changes in learning culture and educational decision-making.
This edited volume “Adoption of Data Analytics in Higher Education Learning and Teaching” is part of the Springer book series “Advances in Analytics for Learning and Teaching” (https://www.springer.com/series/16338) targeting insights into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organisational transformation toward implementation of educational data mining and learning analytics.
We invite broader perspectives regarding the topic of Data Analytics in Higher Education Learning and Teaching, featuring the following themes:
- Theoretical foundation and empirical evidence of the adoption of learning analytics,
- Technological infrastructure and staff capabilities required,
- Institutional governance and policy implementation,
- Case studies that describe current practices and experiences in the use of data analytics in higher education.
Call for Proposals
Prospective authors (co-authors are welcome) are invited to submit a chapter proposal, including title, authors, affiliations, abstract (max. 300 words), five keywords, three key references and a description of how the chapter fits the theme of the book (see above), no later than 15 Mai2019to Dirk Ifenthaler (firstname.lastname@example.org).
The proposed chapter should be a previously unpublished work. Upon acceptance of the chapter proposal, the final chapter should be completed no later than 15 September 2019.Contributions will be blind reviewed and returned with comments by 15 October 2019. Finalised chapters are due no later than 01 December 2019.The final contributions should not exceed 20 manuscript pages. Guidelines for preparing chapters will be sent to authors upon acceptance of the proposal.
The following represents a timeline for completing the edited volume:
- 15 May 2019: Proposal due including title, authors, affiliations, abstract, keywords, references, theme
- 05 June 2019: Notification and additional information for accepted authors
- 15 September 2019: Draft chapters due
- 15 October 2019: Chapters returned with reviewers’ comments
- 01 December 2019: Final chapters due
Please forward your inquires and submissions to:
Professor Dirk Ifenthaler
Curtin University and University of Mannheim
Professor David Gibson