Assessment Consistency Strategies in Large Class Sizes: A GTA Training Pedagogical Perspective

Like other Russell Group universities renowned for their high reputation, quality, and rankings in education and teaching, the University of Manchester faces challenges with large class sizes. These challenges greatly impact the assessment process, as lecturers find it difficult to effectively assess assignments for more than 200 students while meeting deadlines and providing constructive feedback. As a result, multiple graduate teaching assistants (GTAs) are often employed. However, due to the interdisciplinary nature of many disciplines, this approach can lead to inconsistent marking and feedback. This inconsistency leads to students feeling unjustly evaluated, often resulting in requests for reassessment. This process is time-consuming and, more often than not, the marks remain unchanged due to the need for academic justice, further dissatisfying students.
While the current trend in literature leans towards using robots and AI tools for automatic assessment and feedback, which offer quick, consistent grading and feedback, they often result in decreased involvement of GTAs. Given the importance of teaching tasks for GTAs to gain experience and prepare for future work without direct supervision, addressing assessment issues should involve enhancing GTA training through new pedagogical practices. These practices must ensure that GTAs gain valuable experience, students receive consistent and constructive assessment, and crucially, fair feedback that supports learning.
In this study, we aim to apply Sharpe's (2000) GTA training framework. Through a systematic literature review, we will explore assessment practices in large classes, focusing on departmental and faculty training, as well as accreditation. Our goal is to identify gaps and propose practical solutions.