Time: 03:00 PM
Thesis Title: Understanding how automated feedback promotes a sense of belonging, engagement, and academic achievement in higher education
PhD Candidate: Sriram Ramanathan
Please note that this is an open Stage 1 PhD seminar, in which the candidate will summarise their research, prior to a viva with reviewers. Your support and constructive feedback are most welcome!
This is a PhD Stage 1 seminar to present and share the identified gaps in the literature review, proposed research questions, early results, and proposal for further investigations.
An integral component of a student’s journey is about them feeling part of the learning environment and experiencing belonging. Belonging in higher education refers to the sense of connection with the institution which can be amplified by quality relationships with peers, faculty, and the learning community. Research has indicated that students who experience and harbour a clear sense of belonging engage strongly academically, which enhances their achievement of the course learning outcomes. Belonging is multifaceted and needs to be nurtured at various levels of a student’s learning lifecycle. Indicators of belonging include active participation and interactions with the learning management system, peer interactions, and responding to and incorporating feedback in the student learning journey. A sense of belonging can be developed through constructive and timely feedback. Feedback is a constructive platform, grounded on pedagogical intent and has been shown to improve learners’ self-efficacy, promoting engagement along with their sense of belonging. Effective feedback is timely, personalised and induces action. However, providing ongoing and personalised feedback can be challenging for the educator. Learning analytics can offer a solution to overcome this challenge by integrating with the learning management systems and providing messages targeting the inducement of a sense of belonging and engagement that promotes academic achievement. Automated feedback is triggered when students engage with, for example, learning resources and collaborative discussion platforms. Research has indicated that although automated feedback provides useful prompts to learners, there is a lack of evidence on how it promotes engagement, self-efficacy, and especially, a sense of belonging.
The objective of the proposed doctoral thesis is to identify how learning analytics can monitor and support students’ ongoing sense of belonging, and how automated feedback can foster self-efficacy engagement and ultimately, academic achievement. The planned thesis aims to address a) ways to measure the impact of automated feedback on a student’s engagement, self-efficacy, and sense of belonging and b) determine if digital traces can serve as proxies for students’ engagement, self-efficacy, and belonging.
Ram Ramanathan is a PhD student with the Connected Intelligence Centre. Ram brings to the project his experience and passion for teaching, as well as a keen interest in the concept of belonging. He teaches business management subjects with the School of Business at UTS and at Torrens University, Sydney. Ram’s research focuses on how to identify and support students’ sense of belonging through learning analytics.
PhD Supervisors: Simon Buckingham Shum and Lisa-Angelique Lim
Registration: This is an online-only, 1-hour event and registration is required. Please request the Zoom link by emailing the Connected Intelligence Centre (firstname.lastname@example.org) with subject line: PhD Seminar – Request to attend.