About the Project
Alongside this increase in the provision of digital learning platforms, there has been a corresponding rise in interest to understand learners’ behaviour, learning and engagement on these platforms. Educators, developers and researchers are constantly reviewing and analysing the data and the patterns from this to explore how to improve the learner experience. One approach being used is Learning Analytics Dashboards (LADs). Many LADs have the capability to capture course enrolment data, assessments attempts, learner activity details like page clicks, time spent online and drop off rates. However, there are several limitations to LADs discussed in literature. One of the most common ones is the lack of theoretical underpinnings to the learning process, for example, learning science theory. Considering the pivotal role that digital online learning currently has in education, there is a major concern that current LADs do not offer sufficiently rigorous insights into the actual learning process of learners. This is because most of the LADs do not go beyond providing basic learner performance indicators such as aggregated engagement statistics, how much content has been completed, how much time was spent, and how each learner is progressing through the course materials.
This PhD project will combine design approaches with theories in learning sciences to develop and trial a novel Learning Analytics Dashboard, and in the process explore the following question-
• How should LADs be developed to move beyond basic metrics like completion rate of course materials, time spent online, in-course and end of course assessments to more useful indicators of learning?
This research builds on the current work of the research team in Northumbria University’s Digital Learning Lab around remote learning, active learning and technology-enabled learning.
Prospective candidates should have programming experience and ideally an interest in educational technology.
The principal supervisor for this project is Dr. Opeyemi Dele-Ajayi.
Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF21/EE/CIS/DELEAJAYIOpeyimi) will not be considered.
Deadline for applications: 29 January 2021
Start Date: 1 October 2021
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community.
2. Gašević, D., Dawson, S. and Siemens, G., 2015. Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), pp.64-71.
3. Lodge, J.M. and Lewis, M.J., 2012. Pigeon pecks and mouse clicks: Putting the learning back into learning analytics.
Recent publications by supervisors relevant to this project (optional)
Dele-Ajayi, O., Sanderson, J., Strachan, R. and Pickard, A., 2016, October. Learning mathematics through serious games: An engagement framework. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
Dele-Ajayi, O., Strachan, R., Pickard, A.J. and Sanderson, J.J., 2019. Games for Teaching Mathematics in Nigeria: What Happens to Pupils’ Engagement and Traditional Classroom Dynamics? IEEE Access, 7, pp.53248-53261.
Dele-Ajayi, O., Strachan, R., Anderson, E.V. and Victor, A.M., 2019, October. Technology-Enhanced Teaching: A Technology Acceptance Model to Study Teachers’ Intentions to Use Digital Games in the Classroom. In 2019 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.
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