Amrita Vishwa Vidyapeetham Enhances Engineering Programs with Interactive Machine Learning Lab Course
Students Benefit from Instant Feedback and Objective Grading Standards
“MATLAB Grader has simplified student assessment and made the evaluation process transparent. Since they can get feedback on their performance immediately after an assessment, it has also helped in improving student performance as the course progresses.”
Key Outcomes
- MATLAB Grader enabled faster, objective student assessment through a maintenance-free, browser-based environment
- Students received instant feedback on coding tasks and assignments via the interactive platform
- MATLAB helped teachers integrate machine learning–based curricula in their M.Tech courses
Amrita Vishwa Vidyapeetham is a NAAC-accredited A++ grade, multicampus, multidisciplinary teaching and research institution in India. In the past few years, the university’s Department of Electronics and Communication Engineering has integrated lab sessions with various M.Tech courses, such as Machine Learning & Algorithm Design. This course requires students to design, implement, and evaluate machine learning–based solutions for various applications. When conducting the lab sessions, instructors needed a robust strategy to evaluate student performance objectively and quickly verify students’ code and output for lab sessions.
As a solution, Associate Professor Dr. Binoy B. Nair used MATLAB Grader™ and live scripts. Live scripts enriched the learning experience with interactive elements such as sliders, buttons, and integrated media, simplifying complex machine learning concepts. This enabled students to learn, develop, and test machine learning models without the need for extensive coding.
MATLAB Grader also streamlined the assignment process, offering instant grading and feedback, as well as fostering a transparent and objective evaluation environment. Students get immediate access to their grades after each exercise session, which gives them ample time to work on improving their skills before the next test.
To further ease the learning curve, Dr. Nair has provided Classification Learner app, Regression Learner app, and Deep Network Designer app to the course tools, allowing students to create sophisticated machine learning and deep learning systems with minimal coding. This made MATLAB® a preferred environment over Python® for its intuitiveness and ease of use.
Positive feedback from students for the Machine Learning & Algorithm Design course has encouraged Dr. Nair to integrate MATLAB Grader and live scripts into the teaching and learning process of B.Tech undergraduate courses, such as Machine Learning and AI and Cyber Physical Systems.