Focus. This showcase presents the research outcomes of an initiative aimed at reducing student anxiety and improving their wellbeing through flexible deadlines

Background/context. Compared to the general population, university and college students struggle with an exceptionally high prevalence of mental health issues, especially anxiety and depression (e.g., APS 2015; Browne et al. 2017; Larcombe et al. 2016). These challenges are known to affect students’ academic success and university retention rates. In this context, how can teachers address and promote the mental health and well-being of university students?

Description. We explore one source of HE student’s stress and anxiety: deadlines, an issue that intersects with procrastination. We wanted to explore the idea that flexible deadlines could have a positive impact on students’ mental health and wellbeing.

Engagement. Can teachers address and promote the mental health and well-being of university students using semi-flexible deadlines?

Method. Our research is based on qualitative data obtained from surveying 53 students from an Australian research-intensive university. Primary data was thematically analysed using Braun & Clarke’s (2006) framework as reference. Each open-ended question was analysed individually; then, we identified common semantic themes. Data was coded by authors independently and then compared to ensure consistency and reliability in the application of codes and identification of key themes.

Evidence. The availability of simple-to-access flexible deadlines, coupled with self-reflection over why they are being used, can have a positive impact on students, whether they use such ‘extra’ time or not.

Contribution. Even relatively small and limited flexibility in deadlines can reduce students’ stress and anxiety. Students using the ‘original’ deadline also reported reduced stress: the mere availability of flexibility can lead to positive impacts. Flexible deadlines, coupled with self-reflection, are an opportunity for awareness of university initiatives on mental health and wellbeing.

Focus of the showcase:
This presentation reports the outcomes of a research project aimed at understanding the effects of a two-tiered grading approach on student learning motivation within an academic program.

Background/context:
The pandemic and the emergence of generative AI have significantly challenged assessment and teaching practices, sparking a revived curiosity in conventional assessment policies and strategies, along with grading methodologies (Chan, 2022; Chan & Luk, 2022). Grading systems encompass more than just numerical evaluations of student performance, as students engage with grading systems, their perceptions significantly impact their academic journey, shaping their motivation, involvement, and overall satisfaction with learning (Ange et al., 2018; Chamberlin, Yasué, & Chiang, 2023).
A two-tiered grading system simplifies assessment evaluation, resulting in binary outcomes such as Pass/Fail, Satisfactory/Unsatisfactory, or Competent/Not yet competent (Tannok, 2017). Despite the ongoing debates concerning graded versus non-graded systems, there has been relatively limited discussion on this grading approach and its impact on student motivation and learning at the program level, particularly within the evolving landscape of assessment influenced by generative AI.

Description:
This research addresses the research question “What are the effects of implementing a two-tiered grading system on learning motivation of medical students' at different stages of their program of study?”

Method:
Within the case study approach, two primary data collection methods were employed, comprising qualitative data from eight student focus group interviews and two surveys administered to the same cohort of medical students spanning from year 1 to year 5. These data were gathered at different times after all assessments were completed.

Evidence:
This study provides empirical evidence supporting the effectiveness of implementing a two-tiered grading system in fostering positive learning environments, promoting peer learning and collaboration, and alleviating competitive pressures, especially considering the diverse learning needs within the evolving assessment landscape influenced by generative AI.

Background and context. Assessment design is critical for enhanced learning outcomes to meet the dynamic demands of the world of work and to satisfy employer expectations (Herbert, Rothwell, Glover, & Lambert, 2020). Despite the efforts of higher education institutions, current practices have thus far not hit the mark due to several challenges including: 1) tension between authenticity and academic integrity (Ellis et al., 2020); 2) practical limitations of embedding WIL assessment (Ajjawi et al., 2020); and 3) broader challenges in business education.
The initiative/practice. Addressing the above tensions, we developed and implemented a unique, multidimensional framework. This project adopted a six-stage methodological approach to support continuous improvement and offer a way forward in the authentic assurance of graduate capability through assessment tasks.
Methods of evaluative data collection and analysis. Both quantitative and qualitative methodologies were adopted for data collection and analysis. Quantitative analysis entailed the review of assessments and the qualitative analysis involved thematic analysis of data from student focus groups.
Evidence of outcomes and effectiveness. The implementation of the framework resulted in the identification of barriers, enablers, benefits and opportunities for scaling up. This led to the assessment classification framework being systematically used across the entire business school.
Contribution to scholarship. The framework offers a practical approach to assuring graduate capability through assessment which will prove useful to academics, assessment designers and T&L staff.
Engagement. The presentation will pose reflective questions for the audience to consider.