  The demand for efficient and accurate assessment methods has intensified as
education systems transition to digital platforms. Providing feedback is
essential in educational settings and goes beyond simply conveying marks as it
justifies the assigned marks. In this context, we present a significant
advancement in automated grading by introducing Engineering Short Answer
Feedback (EngSAF) -- a dataset of 5.8k student answers accompanied by reference
answers and questions for the Automatic Short Answer Grading (ASAG) task. The
EngSAF dataset is meticulously curated to cover a diverse range of subjects,
questions, and answer patterns from multiple engineering domains. We leverage
state-of-the-art large language models' (LLMs) generative capabilities with our
Label-Aware Synthetic Feedback Generation (LASFG) strategy to include feedback
in our dataset. This paper underscores the importance of enhanced feedback in
practical educational settings, outlines dataset annotation and feedback
generation processes, conducts a thorough EngSAF analysis, and provides
different LLMs-based zero-shot and finetuned baselines for future comparison.
Additionally, we demonstrate the efficiency and effectiveness of the ASAG
system through its deployment in a real-world end-semester exam at the Indian
Institute of Technology Bombay (IITB), showcasing its practical viability and
potential for broader implementation in educational institutions.
