Abstract:

Evaluating practical and laboratory activities in large university cohorts poses significant challenges related to workload, consistency, fairness, and the ability to provide meaningful feedback.

As student numbers grow, teaching teams increasingly require strategies that ensure high‑quality assessment while maintaining sustainability. This study presents a set of best practices for assessing practical work in large‑enrollment engineering courses, based on multi‑disciplinary implementation and evidence‑informed approaches.

The proposed model is designed combining approaches to analyse the effects of the rubrics and feedback on grading practices. Students in advance were given access to the rubrics to guarantee their understanding of assessment criteria. The data sources are mainly from student’s previous courses results as the comparison assessment outcomes and student perceptions of the structured evaluation tools by for example course surveys to measure clarity of expectations and satisfaction with feedback.
The context of implementation is in higher education context, within industrial engineering master courses, characterised by very large student’s groups and many lecturers involved in assessment. Structured rubrics and standardised feedback assessment is introduced as part of optimize assessment quality and improving alignment with learning outcomes.

The results show that structured rubrics and standardised feedback formats reduce inconsistencies across graders and improve students’ perceptions of fairness. Integrating peer‑assessment and guided self‑assessment increases engagement and reinforces students’ ability to judge the quality of their own work. Sampling strategies, such as evaluating key sections of lab notebooks or selected milestones in longer projects, have also proven effective in balancing depth and feasibility. Learning analytics provide further insights, enabling instructors to identify recurrent errors, detect at‑risk students, and adjust teaching strategies in real time.

Faculty involved in the implementation highlight the importance of coordinated teamwork, clear distribution of assessment tasks, and the use of technology to streamline logistics. The proposed model offers a transferable and flexible framework that supports fair, efficient, and pedagogically meaningful evaluation in large practical courses. Its adoption can help institutions enhance learning outcomes while ensuring sustainable assessment practices in high‑enrollment settings.

Keywords:

Large cohorts, practical work assessment, rubrics, learning analytics.