A framework for strategic planning of data analytics in the educational sector

Masters thesis


Tsiakara, A. 2022. A framework for strategic planning of data analytics in the educational sector. Masters thesis Middlesex University Computer Science
TypeMasters thesis
TitleA framework for strategic planning of data analytics in the educational sector
AuthorsTsiakara, A.
Abstract

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices.
Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level.
The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations.
The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection.
The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study.
The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages.
At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment.
Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics.

Sustainable Development Goals4 Quality education
9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Department nameComputer Science
Institution nameMiddlesex University
Publication dates
Print06 Jan 2023
Publication process dates
Deposited06 Jan 2023
Accepted30 May 2022
Output statusPublished
Accepted author manuscript
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/8q378

Download files


Accepted author manuscript
  • 79
    total views
  • 77
    total downloads
  • 1
    views this month
  • 2
    downloads this month

Export as

Related outputs

Hybrid educational environments – using IoT to detect emotion changes during student interactions
Nalli, G., Dafoulas, G., Tsiakara, A., Langari, B., Mistry, K. and Tahmasebi Aria, F. 2023. Hybrid educational environments – using IoT to detect emotion changes during student interactions. Interaction Design and Architecture(s). 58 (1), pp. 39-52. https://doi.org/10.55612/s-5002-058-001
Improving educational and training programmes through learning analytics and visualisation of educational data
Dafoulas, G. and Tsiakara, A. 2020. Improving educational and training programmes through learning analytics and visualisation of educational data. AUDIT Scientific-Practical Journal. 28 (2), pp. 22-29.
The role of data analytics in managing higher education quality
Dafoulas, G. and Tsiakara, A. 2019. The role of data analytics in managing higher education quality. International Conference on Actual Problems of Modern Nature and Economic Sciences. Ganja, Azerbaijan 03 - 04 May 2019
Offering smarter learning support through the use of biometrics
Dafoulas, G., Samuels-Clarke, J., Cardoso Maia, C., Ali, A. and Tsiakara, A. 2019. Offering smarter learning support through the use of biometrics. 26th International Conference on Telecommunications (ICT 2019). Hanoi, Vietnam 08 - 10 Apr 2019 IEEE. pp. 270-274 https://doi.org/10.1109/ICT.2019.8798863
Evaluating the use of augmented reality in learning portfolios for different team roles
Dafoulas, G., Tsiakara, A., Cardoso Maia, C. and Neilson, D. 2019. Evaluating the use of augmented reality in learning portfolios for different team roles. ICICS 2019: 10th International Conference on Information and Communication Systems. Irbid, Jordan 11 - 13 Jun 2019 IEEE. pp. 173-178 https://doi.org/10.1109/IACS.2019.8809172
Investigating patterns of emotion and expressions using smart learning spaces
Dafoulas, G., Tsiakara, A., Samuels-Clarke, J., Cardoso Maia, C., Neilson, D. and Ali, A. 2019. Investigating patterns of emotion and expressions using smart learning spaces. ICICS 2019: 10th International Conference on Information and Communication Systems. Irbid, Jordan 11 - 13 Jun 2019 IEEE. pp. 238-244 https://doi.org/10.1109/IACS.2019.8809119
Google Glass as a learning tool: sharing evaluation results for the role of optical head mounted displays in education
Dafoulas, G., Cardoso Maia, C. and Tsiakara, A. 2018. Google Glass as a learning tool: sharing evaluation results for the role of optical head mounted displays in education. 12th International Conference on Interfaces and Human Computer Interaction. Madrid, Spain 18 - 20 Jul 2018 IADIS. pp. 67-74