Learning analytics as a core component for higher education disruption: Governance stakeholder
Date
2017
Embargo
Advisor
Coadvisor
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Volume Title
Publisher
ACM
Language
English
Alternative Title
Abstract
Higher education institutions are at this stage, on the one hand, faced with challenges never seen before and, on the other hand, their action is moving very rapidly into digital learning spaces. These challenges are increasingly complex because of the global competition for resources, students and teachers. In addition, the amount of data produced inside and outside higher education institutions has grown exponentially, so more and more institutions are exploring the potential of Big Data to meet these challenges. In this context, higher education institutions and key stakeholders (students, teachers, and governance) can derive multiple benefits from learning analytics using different data analysis strategies to produce summative, real-time and predictive information and recommendations. However, it may be questioned whether institutions, academic administrative staff as well as including those with responsibility for governance, are prepared for learning analytics? As a response to the question raised in this paper is presented an extension of a disruptive conceptual approach to higher education, using information gathered by IoT and based on Big Data & Cloud Computing and Learning Analytics analysis tools, with the main focus on the stakeholder governance.
Keywords
Disruption, Higher Education Institutions, Learning analytics, Governance
Document Type
conferenceObject
Publisher Version
10.1145/3144826.3145387
Dataset
Citation
Moreira, F., Gonçalves, R., Martins, J., Branco, F., & Au-Yong-Oliveira, M. (2017). Learning analytics as a core component for higher education disruption: Governance stakeholder. In Proceedings of the International Conference Technological Ecosystems for Enhancing Multiculturality (TEEM2017), Cádiz, Spain, October 18-20 2017. doi: 10.1145/3144826.3145387. Disponível no Repositório UPT, http://hdl.handle.net/11328/2407
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TID
Designation
Access Type
Open Access