A sense of belonging and personal connection can be argued as essential for both staff and students alike regardless of the learning context. In this pandemic era, 'Relationship-rich education' philosophies for universities are considered more vital than ever due to increased online learning (Felten & Lambert et al., 2020). To-date, higher education students' sense of belonging is a concept that has not been adequately conceptualised. The current literature on 'sense of belonging' spans a number of disciplines, with no apparent consensus on definition between these, complicated by the fact that sense of belonging is temporal and context-sensitive such as during COVID-19. Whilst there has been recent studies outlining the student experience of belonging in the online space (Peacock et al., 2020), investigation of teacher/educator experiences have been relatively overlooked. This presentation will present interim findings from the SoE CSSP that explored students' sense of belonging to their education units.

‘What do I know now that I didn’t know before?’ – for those of us who are teachers, how many times have we posed this question to our students? We ask our students this question to check on their knowledge and understanding; and interestingly, this is a question I asked myself all throughout my teaching career. Perhaps, an even better question would have been ‘What do I know now that I needed to know before?’ There were many times in my later years as a secondary English and History/Geography teacher where I found myself thinking.

The Commencing Student Success Program (CSSP) comprises 13 evidence-based strategies for online engagement underpinned by the Universal Design for Learning principles of inclusive access to improve learner retention and engagement (Sasson et al., 2021), promoting first year student engagement and retention at the tertiary level. The program's impact has led to it being adopted across the University's School of Education as its signature pedagogy. The University's unique regional context provides education to a high number of students studying by distance; often rural, mature-age, or first-in-family. Applying these strategic elements into online course design ensure a sense of support and connection is embedded, and reducing sense of isolation, throughout their studies.

Online learning is a well-established mode for tertiary education and training. However, technology-mediated learning at a distance continues to face challenges to participant engagement, despite the proliferation of models of effective online learning and the implementation of increasingly sophisticated technologies to enhance learning(Bragg, Walsh & Heyeres, 2021). The critical need to build and maintain relationships for effective learning continues to be confounded by the geographic and temporal distribution of online participants as well as the nuances of the technology in use. Online learning occurs in collaborative text-spaces within learning management systems, social media spaces and their attendant learning objects such as forums, many of which foreground language as the primary resource for making meaning with others. As such, ongoing challenges to engagement in online learning may be viewed with fresh eyes by considering the language-based interpersonal affordances of these text-spaces. This pecha kucha presentation examines how the language choices made by learning facilitators impact on student engagement in online learning. It draws on a case study of the strategic use of evaluative language –the language used to express feelings and build relationships -by one teacher educator to engage initial teacher education students in online learning. Underpinned by the Systemic Functional Linguistic model of language (Halliday & Matthiessen, 2014) and following Martin & White (2005), appraisal analysis of weekly forum posts across three iterations of a unit of study shows how changes to language choices made by the teacher educator positively impacted on student engagement. This illuminates the relationship between online learning and language, and how judicious use of language-based meaning making resources can be used to improve online participant engagement.

The pandemic caused many disruptions in the teaching and learning of students worldwide. Discussions continue today about its impacts and the ways in which teachers, students, and schools responded with the movement to online learning.

Although the World Health Organisation has declared that COVID-19 is no longer a public health emergency of international concern, it is paramount that we consider the lessons we gained from this time as we move into a post-pandemic world. One such important consideration is the ways in which teachers can stay connected to their students in the digital space, ensuring that students remain engaged in the learning.

Artificial intelligence (AI) technology, such as Chat Generative Pre-trained Transformer (ChatGPT), is evolving quickly and having a significant impact on the higher education sector. Although the impact of ChatGPT on academic integrity processes is a key concern, little is known about whether academics can reliably recognise texts that have been generated by AI. This qualitative study applies Turing's Imitation Game to investigate 16 education academics' perceptions of two pairs of texts written by either ChatGPT or a human. Pairs of texts, written in response to the same task, were used as the stimulus for interviews that probed academics' perceptions of text authorship and the textual features that were important in their decision-making. Results indicated academics were only able to identify AI-generated texts half of the time, highlighting the sophistication of contemporary generative AI technology. Academics perceived the following categories as important for their decision-making: voice, word usage, structure, task achievement and flow. All five categories of decision-making were variously used to rationalise both accurate and inaccurate decisions about text authorship. The implications of these results are discussed with a particular focus on what strategies can be applied to support academics more effectively as they manage the ongoing challenge of AI in higher education.