Analysis of Automated and Personalized Student Feedback to Improve Learner Experience


Due to the vast amounts of data generated at educational institutions and need for teachers to personalize feedback to every student, having an automated feedback system to support educators is important. Data research teams at the Atlantic Technological University have developed an automated feedback system that sends lecturer feedback to student based on their performance and learning patterns. This was developed using a combination of different technologies from the application of python programming, data cleansing, and API link using Microsoft Power Automate. This paper reports on the student feedback from this system and their experiences reading them. Data gathered from students regarding their experiences in educational institutions is known as student feedback. This feedback can be expressed in speech, writing, or gestures. Additionally, it is utilized by organizations or educators to implement changes to current practices. Students lose focus on the goal of learning when grades are attached to every assignment, whether it be summative or formative, and instead adopt the mindset that they must perform with mastery even from the first time they tackle an issue. This frequently prompts students to look for holes and short cuts to get a decent mark. They avoid learning so that, even if they have not grasped the content, they are more likely to succeed and receive the highest grades. The only way to escape from this grade-oriented fixation is through a fundamental educational change that emphasizes the value of feedback in student learning rather than relying on grades and results. Data used for this pilot study is taken from a selection of first year students (n=206) and this paper discusses the methods used to automate the personalized student feedback and reports on the student experience of the system.

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

Paper Information
Conference: PCE2023
Stream: Learning Experiences

This paper is part of the PCE2023 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window

To cite this article:
Ogbuchi I., Kiely E., Quigley C., & McGinty D. (2023) Analysis of Automated and Personalized Student Feedback to Improve Learner Experience ISSN: 2758-0962 The Paris Conference on Education 2023: Official Conference Proceedings
To link to this article:

Comments & Feedback

Place a comment using your LinkedIn profile


Share on activity feed

Powered by WP LinkPress

Share this Research

Posted by James Alexander Gordon