This new paper from DER member Prof Michael Henderson reveals new insights into the factors that influence the tempation to cheat in large scale digital exams.

Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on those who do not cheat, but who are tempted to do so. We conceptualise this group as being at risk of future cheating behaviour and potentially more receptive of targeted strategies to support their integrity decisions. In this paper we report on a large-scale survey of university students (n = 7,511) who had just completed one or more end of semester online exams. In doing so we explore students’ reported temptation to cheat. Analysis surrounding this “at risk” group reveals students who were Tempted (n = 1379) had significant differences from those who Cheated (n = 216) as well as those who were Not tempted (n = 5916). We focus on four research questions exploring whether there are specific online exam conditions, security settings, student attitudes or perceptions which are more strongly associated with the temptation to cheat. The paper offers insights to help institutions to minimise factors that might lead to breaches of assessment integrity, by focusing on the temptation to cheat during assessment.

Access the article here!

Citation: Henderson, M., Chung, J., Awdry, R. et al. The temptation to cheat in online exams: moving beyond the binary discourse of cheating and not cheating. International Journal of Educational Integrity 19, 21 (2023). https://doi.org/10.1007/s40979-023-00143-2

This new article by DER member Prof Michael Henderson reveals some of the productive as well as challenging practices surrounding PhD supervision feedback.

PhD candidates, like all students, learn through engaging with feedback. However, there is limited understanding of how feedback strategies support doctoral candidates. This qualitative framework synthesis of 86 papers analysed rich qualitative data about feedback within PhD supervision. Our synthesis, informed by sociomateriality and a dialogic, sense-making view of feedback, underscores the critical role that feedback plays in doctoral supervision. Supervisors, through their engagement or disengagement with feedback, controlled candidates’ access to tacit and explicit standards. The ephemeral and generative nature of verbal feedback dialogues contrasted with concrete textual comments. While many supervisors aimed for candidates to become less reliant on feedback over time, this did not necessarily translate to practice. Our findings suggest that balancing power dynamics might be achieved through focussing on feedback materials and practices rather than supervisor-candidate relationships.

Access the article here!

Citation: Bearman,M., Joanna, T., Henderson, M., Esterhazy, R., Mahoney, P., & Molloy, E. (2024). Enhancing feedback practices within PhD supervision: a qualitative framework synthesis of the literature. Assessment & Evaluation in Higher Education, DOI: 10.1080/02602938.2024.2307332

New article in the Cambridge Journal of Education by DER members Luci Pangrazio, Neil Selwyn & Bronwyn Cumbo exploring student experiences of school datafication

ABSTRACT
The use of digital technologies within schools is leading to the increased generation, processing and circulation of data relating to students. To date, academic research around this ‘datafication’ of schools and schooling has tended to focus on institutional issues of governance and commercialisation, with relatively little consideration of students’ experiences. Drawing on focus group discussions with 62 students across three Australian secondary schools, the paper explores students’ experiences of school datafication in terms of power, surveillance and affect. It highlights students’ relatively constrained and distanced relations with school technology use, schools’ use of data to enforce student accountability and self-regulation of behaviour, as well students’ perceived powerlessness to engage agentically in digital practices. Drawing on notions of ‘digital resignation’ and ‘surveillance realism’, the paper concludes by considering the extent to which students might be supported to meaningfully engage with (and possibly resist) the constraining ‘atmospheres’ of datafication.
Luci Pangrazio, Neil Selwyn & Bronwyn Cumbo (2023) Tracking technology: exploring student experiences of school datafication, Cambridge Journal of Education, https://doi.org/10.1080/0305764X.2023.2215194

New systematic literature review on instructional video design principles.

Authors: Matt Fyfield, with DER members Michael Henderson and Michael Phillips

The most common theoretical lens used to design and evaluate instructional videos has been to apply principles emerging from the cognitive theory of multimedia learning. However, these principles have been largely developed from research using instructional media other than videos. In addition, there is no comprehensive list of principles that have been shown to improve learning from instructional videos. Therefore, this paper seeks to identify principles of video design that are empirically supported in the literature.

This article provides useful guidance for instructional designers creating educational video content.

In addition to describing the breadth of research in the field, this paper also found that the development of the research field suffers from a lack of coherence and is in urgent need of clear nomenclature and improved reporting of media and research design.

Cover image: Photo by George Milton

DER’s Carlo Perrotta and Neil Selwyn have a new article in New Media and Society, which takes a close look at “language models”, complex AI systems like GPT-3 that can simulate human communicative competence in a number of tasks, such as writing and creative composition. The article is critical of these claims and argues instead that the ‘deception’ of these technologies relies on constant human labour which must compensate for and moderate the AI’s shortcomings and, most importantly, the numerous biases that it inherited from its “training data”. Check out the downloadable file (accepted manuscript) here.

Photo by Mauro Sbicego on Unsplash