HEEL‑seminarium/HEEL Seminar
Välkommen till en onlineseminarie-serie för e-lärande/teknikstött lärande där utbildning, undervisning och forskning om högskolan är i fokus.
Varmt välkomna till årets femte HEEL-seminarium. Temat den 21 oktober är hur generativ AI kan bidra till utveckling av undervisning. Den första presentationen fokuserar på hur large language models och generativ AI kan bidra till att utveckla arbetet med studenters kursvärderingar. Euan Lindsay från Ålborgs universitet presenterar ett pågående arbete. Den andra presentationen är från Jönköping university. Ylva Lindberg, Anna-Lena Godhe och Maria Bäcke studie berör hur studenters kollaborativa lärande och skrivande kan inkludera generativ AI.
Datum: 21 oktober, 2024
Tid: 10.00 -12.00 (Swedish time)
Plats: Online via Zoom
Frågor? Kontakta Professor Jimmy Jaldemark
Information in english
Most welcome to the fifth HEEL seminar of 2024. Anyone interested in issues of digital technology, learning and higher education might benefit from participating. The theme for the HEEL seminar on the 21st of October is how generative AI can contribute to educational development.
The first presentation is from Euan Lindsay, Aalborg University. He discusses how large language models can leverage actionable course evaluations. The second presentation is from Ylva Lindberg, Anna-Lena Godhe and Maria Bäcke, Jönköping University. Their study concerns how generative AI impact can impact students’ collaborative learning and writing.
Date: October 21, 2024
Time: 10.00 - 12.00 (Swedish time)
Place: Online via Zoom
Questions? Please contact Professor Jimmy Jaldemark
Abstracts
Leveraging large language models for actionable course evaluation: Student feedback to lecturers
Euan Lindsay, UCPBL, Aalborg University
End of semester student evaluations of teaching are the dominant mechanism for providing feedback to academics on their teaching practice. For large classes, however, the volume of feedback makes these tools impractical for this purpose. Our work explores the use of open-source generative AI to synthesise factual, actionable and appropriate summaries of student feedback from these survey responses. Our results reveal a promising avenue for enhancing teaching practices in the classroom setting. Our contribution lies in demonstrating the feasibility of using generative AI to produce insightful feedback for teachers, thus providing a cost-effective means to support educators' development. Overall, our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting
Diffractive creation: Students’ collaborative writing processes and practices with GenAI
Ylva Lindberg, Anna-Lena Godhe & Maria Bäcke, Jönköping University
This presentation will focus on the analytical work with data collected from upper secondary school students’ collaborative writing processes and practices with GenAI during a one-day- event designed by teachers as a Futures Day 2024 (cf. Lindberg & Haglind, 2024). Students in the Swedish technology programme were instructed to create stories about futures and technologies, using AI tools for text and image creation. They started their writing process by discussing the plot, setting, and main characters with their peers. AI tools were then used to generate texts and images. In this stage, texts were rewritten, and prompts were tweaked to generate texts and images corresponding to what the students had in mind. Screen recordings, sayings, and doings in four student groups were collected during the Futures Day. Based on the data from two groups, and to contribute to L1 postdigital education, we attempt to theoretically discuss how to conceptualize and analyze algorithmic authorships (Henrickson, 2021). Doing so, we draw on poststructuralist and postmodernist scholarships, specifically diffractive thinking (Barad, 2007), that deconstructs relationality to observe the co-establishment of (identity) positions while underscoring situations of reduction and assimilation that erases difference (Haraway, 2004). The concept of diffraction (Barad, 2014) targets differences as “a relation of differences within” that may come together or rather diffract. Diffraction is engaged as a way of observing how practices and processes simultaneously diverge and converge in collaborative writing with AI tools.