My name is Doctor Vikran Kumar. I have completed my. A PhD from Kalina Instrument Industrial Technology.

Professor Kumar, when you think about stepping into a classroom for the first session of a new course, what’s one foundational concept or topic you especially look forward to introducing to your students, and why?

Then I started my PhD 2018 and completed in 2024. Overall, I have 14 years of teaching experience.

Right, so you've been teaching for quite a while. In those 14 years, what kind of teaching environment have you found most effective—large lectures, small seminars, lab-based sessions, or something else?

At the start of my career when technology was not so much advanced in in in early. 2011 or 2015?

So there were normal lectures would be there, large lectures, the seminars would be. For the best part for the students. Later I will come to 2020 season, then we have this. Uh, lab sessions. More and more lab sessions. Hands on experience.

Okay, so in those early years, before technology was widely used in classrooms, how did you structure your sessions to keep students engaged and ensure they really understood the material?

So you shifted from mostly lectures and seminars to more lab sessions and hands-on work as technology evolved. In those lab sessions, what strategies did you use to make sure every student actually engaged with the material rather than just following instructions passively?

First of all, what I have done is that in a in the entire class I have devoured the student based on three categories. One is green. One is orange, uh, yellow zone and one is red zone. Green Zone students are the one who are very bright. Run so have. Have given a separate set of tasks to them.

Yellowstone students are average students, so we have another medium level. Programs were given to them. And for Red jewel friends are low level students where, where where where basic level of programming was given so that they will evolve from basic level of program to work. Higher level. For the Green Zone front, there was a real real. Real time case studies. Lab program were given to them.

Professor, I noticed you've published a paper titled "Enhancing Software Effort Estimation Through Stacked Deep Learning Models" in the International Journal of Intelligent Systems and Applications in Engineering. How have insights from that research influenced the way you teach advanced topics like machine learning or AI in your classroom, especially to students who may not have a strong mathematical background?

That have divided the program into 3 categories based on the. Green Zone, Yellow zone and red zone. Green Zone for mainly for the top of season having 8.5 CJP and above. Hello Zone R for the majority of people. CGPR between 6.5 to 7.5. And redzone are the people who are wearing having less than 6.5. Sweat the those who are weak in studies.

That's an interesting approach—tiering the tasks by student ability. When you designed those real-time case studies for your advanced students, can you describe one example and explain how you assessed whether they truly understood the underlying concepts, not just the coding?

That breakdown by CGPA is quite systematic. For your advanced students in the Green Zone, you mentioned giving them real-time case studies—can you walk me through one specific example you used, and how you evaluated their understanding beyond just code correctness?

Yeah, for one of the subjects, I'm for BDA subjects at the business. Business. Data analytics subject. What I've done is that I told them to go to any supermarket. Any supermarket, Spencer more or whatever? They are local in their uh. In that area. Explore the low supermarket, how they have arranged the items, how how many racks were there? Is there any items which are related to them? What are the any drawbacks we have figured in those items there there I have given a case study to them. And have went to the supermarket, had their case study, have interacted with the market and really came up with a good solution.

One of the examples of a good solution is that how can you? Reduce the queue line. Because in abroad do you have a QR code? Scanning system where you can directly barcode the. You're cut off all the items and you can pay the bill directly, but in India, but in India. That technology has not been so much developed into it. So we have this queue like normal queue lengths of various things. And then they. Them sorted out that OK, we can also implement that. They can barcode all the items whatever they are taking so the. Final price will be. Uh, in an app their final price will be reflect just you scan that app. And in the other person POS system it will complete all the IT list of items. And the corresponding wheel of that and Bill can create in a faster approach.

Interesting. So after these students returned from the supermarket with their case studies, how did you guide the classroom discussion to connect their observations back to core concepts in business data analytics?

That's a solid example—getting students to analyze a real-world problem and propose a tech-driven solution. When you assessed their work, did you focus more on their technical implementation, their creativity, or their ability to connect the solution back to core analytics principles?

Uh, not much on the technique, uh, implementations, but on the creativity and how they interact with the people, that's what they want and how to. How to? Uses technology to make it very simpler for the. Uh, larger section of the.

Okay, let's shift our focus to your research. Given your background in business data analytics, where do you see the most fundable research directions in your area over the next few years? Which specific grants or funding sources would you target to support that work?

Yeah, I'm done.

Let's turn to your teaching approach, Professor. Suppose you're teaching a class of 200 students in your area of expertise, but you can't use slides or traditional lectures. How would you design a session that keeps everyone engaged and actively learning?

Basically. I'll I'll start my session with a recap of what. Yesterday's session was. I'm going to uh. Make some friends, no? Stand up in a random number voice. I go to ask you some questions. If they are unable to answer, I'll make them to choose any of their friends who can answer on their behalf. So for the first. In one hour class for 15 minutes, I'll make them to answer my questions, whatever, I will give a recap and I will make them to show. Then answer my questions or if they're unable to answer, so that somebody will answer and they can understand in the session what happened. And through that questions, I will continue with the Today session.

And try to explain how they are interrelated both and and which better way we can. They can explain and they can ask. And after one of. One the session is done, I'll give them some. Uh, some theoretical or or, uh. Please type.

Accreditation cycles can be pretty demanding. If you joined our department and discovered that outcome assessment data was inconsistent across different courses, how would you go about fixing that?

Imagine this scenario, Professor: one of your students files a formal complaint alleging your grading was biased, while at the same time your department head is urging you to improve pass rates. How would you handle this situation and balance academic integrity with institutional pressure?

BS Next question please.

Given your experience in business data analytics, do you have industry connections that could help your students land internships or placements—maybe through consulting, collaborations, or prior projects?

Actually, uh. Uh, yeah, I have. Started connecting with one of the company called as Sindel Technologies in Chennai. UH talks are going on. There was an industrial to visit for the students. What their products are. How they can relate to their BDS. And working on and trying to work on some memories with the other companies where the big day analytics. Actually work how they work. And some sort of. Mini project or a major project? Uh. In that. BDA domain so that we with the local faculty. Or the university faculty can help them to how to solve the major problems or. Uh, current issues problem.

Professor, let's talk about your research expertise. Can you describe a specific multimedia or AI project you've led or contributed to, and what made your role in it essential?

Robert Rack project. On a paper which have submitted to Scientific Scientific Reports.

Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?

Can you further move it to the? To a future worker, how can what's the major role, how it works actually in future also?

Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?

Sorry there are little hiccups. What actually it is looking for?