Abstract
A critical strategy of motivating students and improving performance in higher education is communicating timely and personalized feedback (Koenka et al., 2019). The language used to deliver students' progress and what specific intervention can support their learning is hugely impactful especially for students who are struggling. This can also be challenging for the academic community to implement when lecturing to large cohorts of year 1 students. This paper presents how the learning analytics team in ATU Galway have developed a data pipeline to ensure students receive appropriate and personalized feedback on their progress in year 1 Science and Computing modules. This work initially began with the DANIEL project in 2015 which employed a semi-automated process and has evolved to a streamlined automated process embedding the tools of the MS Power environment. This research output is the result of a close collaboration with academics, researchers and the Computing Services team transforming Moodle data into meaningful information and insights for students. The step-by step process of how this is achieved using Power Apps (lecturer interface for feedback and progress thresholds), Power Automate to trigger large scale communication, Power BI (visualization of cohorts' performance). Learners have engaged as partners in the development at each phase of the process and their experiences of this transformed digital learning feedback systems are explored.
Author Information
Ikechukwu Ogbuchi, Atlantic Technological University, Ireland
Etain Kiely, Atlantic Technological University, Ireland
Cormac Quigley, Atlantic Technological University, Ireland
Donal McGinty, Atlantic Technological University, Ireland
Konrad Mulrennan, Atlantic Technological University, Ireland
John Donovan, Atlantic Technological University, Ireland
Paper Information
Conference: ECE2023
Stream: Design
This paper is part of the ECE2023 Conference Proceedings (View)
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To cite this article:
Ogbuchi I., Kiely E., Quigley C., McGinty D., Mulrennan K., & Donovan J. (2023) Using Power Tools to Automate and Scale Personalized Feedback to Learners ISSN: 2188-1162 The European Conference on Education 2023: Official Conference Proceedings https://doi.org/10.22492/issn.2188-1162.2023.108
To link to this article: https://doi.org/10.22492/issn.2188-1162.2023.108
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