How to Shortlist for Student Track?

The MARVEL R&D Lab Student Track Program has been established to provide students a structured opportunity to learn, grow, and innovate regardless of their academic background, department, or prior exposure. The intent is to support students who demonstrate curiosity, self-initiative, and the willingness to learn and contribute to real engineering outcomes.

As we begin shortlisting candidates for Batch 8, which has received 212 applications across six domains — Artificial Intelligence and Machine Learning, Internet of Things, Design & Prototyping, Cloud Computing, Cybersecurity, and Aviation — it is important that every domain coordinator follows a consistent, transparent, fair, and merit-based evaluation process.

This guide outlines the recommended approach.

1. Determine Domain Intake Capacity

Each domain must determine the number of students it can reasonably accommodate based on:

• Availability of physical resources
• Active projects and research continuity
• Mentorship and supervision capacity

Typical intake per domain ranges between 5–10 students. Increasing or reducing capacity must be supported with a clear rationale.

The shortlisting process generally takes about one week when executed systematically.

2. Prioritize Domain Fit

Begin by filtering candidates who selected your domain as their first preference, as they are most likely to remain consistent through the coursework. Consider second and third preferences only if necessary.

3. Objective Evaluation Based on Tangible Work

Give priority to measurable achievements rather than perceived intent alone, including:

• Prior projects (academic or personal)
• DIY or prototype work
• Hackathon / workshop participation
• Internship or hands-on exposure
• Active GitHub / portfolio / design documentation
• Resume quality and completeness

These indicators demonstrate initiative, capability, and potential contribution to the lab ecosystem.

4. Review Qualitative Responses

Evaluate subjective questions such as:

• “Why do you want to join the MARVEL Lab?”
• “How do you plan to contribute back?”
• Long-term goals and motivation

Give weight to clear, thoughtful, original responses. Generic statements should carry minimal influence.

5. Avoid Bias

To maintain fairness:

• Avoid considering branch, year, prior association, or friendships.
• Where possible, hide identity-based columns during evaluation.
• Focus on merit, consistency, and growth potential.

6. Diversity Consideration

Only once the shortlist is prepared, reflect on the composition as a team to ensure:

• Gender diversity
• Diversity of academic backgrounds and branches
• Diversity across years of study

While merit remains the highest priority, diversity should not be overlooked and can be given appropriate weight to ensure healthy collaboration, broader perspectives, and creative innovation within the lab.

Give slight additional priority to students in earlier years of their degree, as they are more likely to stay through the program and contribute longer-term.

7. Cross-Domain Recommendations

If a candidate does not align with your domain but is strong in another:

• Share the application with the respective domain coordinator.
• Prevent losing potential talent due to domain mismatch.

8. Telephonic Discussions (Optional)

If you believe an applicant requires better understanding beyond their written form:

• You may conduct a brief telephonic or virtual interaction
• Keep the discussion short and structured, strictly for clarification

This should be used sparingly and fairly.

9. Weighted Evaluation Rubric

To maintain consistency and reduce conflict, the following scoring rubric may be used:

Evaluation Category	Description	Weightage
Practical Work & Projects	Hands-on builds, prototypes, academic/personal projects, GitHub activity, portfolio quality	35%
Initiative & Consistency	Workshops, hackathons, online courses, independent learning	20%
Technical Potential & Domain Alignment	Motivation, clarity, first preference relevance, growth potential	15%
Resume & Documentation Quality	Structure, completeness, professional clarity	10%
Contribution to Lab Ecosystem	Collaboration potential, willingness to contribute back, communication	10%
Year & Diversity Consideration	Likelihood to stay through the program; diversity factors	10%

Final decisions should be based on weighted scores rather than equal distribution.

10. Post-Shortlist Procedure

Once the shortlist is finalized:

1. Assign one coordinator to compile and submit the final list.
2. Inform and take approval from the Faculty Coordinators before public announcement.
3. Create a WhatsApp communication group for the selected students.
4. Conduct the official Batch Orientation.
5. Encourage all applicants (including those not selected) to join the Open Learner Program (OLP).
6. Conduct an orientation for OLP participation and benefits.
7. Prepare and distribute Level-0 kits.
8. Start coursework only after orientation completion.

If there are missing resources or required components in any domain, prepare an itemized list and inform the Finance Coordinator before the program begins.

Conclusion

The Batch Program seeks not only the most technically advanced candidates but those who demonstrate:

• Consistent curiosity and learning ownership
• Reliability, discipline, and commitment
• A willingness to contribute to a collaborative ecosystem
• The drive to add value beyond their personal learning outcomes

Our objective is to identify students who will make the most of the opportunity and continue contributing meaningfully to the lab community.

Happy shortlisting, and feel free to reach out in case of any doubts or clarifications.