Innovations in Teaching Learning
| Sl.No | Innovative Teaching Strategy | Faculty Name |
| 1 | Course Website – Python Programming | Justin Mathew |
| 2 | Industry Sessions by Experts | Justin Mathew, Gokulnath G |
| 3 | Gamification in Learning | Justin Mathew |
| 4 | Flipped Classroom | Gokulnath G,Jinu Thomas,Ria Mathews |
| 5 | Usage of ICT tools/Coding platform | Jerrin Sebastian,Thomas Joseph,Dr. Jo Cheriyan |
| 6 | Case Based Learning | Dr. Anju Pratap |
| 7 | Youtube Video Lectures/Youtube Channel | Safad Ismail/Justin Mathew |
| 8 | E Classroom/Doc Share | Safad Ismail |
| 9 | Education Blog | Safad Ismail |
| 10 | Peer Learning | Sheeba Babu |
| 11 | Think Pair Share | Soumya Sara Koshy |
| 12 | Peer Learning and Collaborative Teaching | Soumya Sara Koshy |
| 13 | One-Minute Paper | Soumya Sara Koshy |
| 14 | Inquiry Based Learning | Soumya Sara Koshy |
| 15 | Concept Quiz | Soumya Sara Koshy |
| 16 | Blended Learning | Dr. Shibu K R |
- Course Website with Python Interpreter tool designed for Python Programming Course
| Faculty Name | Website | Course | Outcome |
| Justin Mathew | https://jmat-dev.github.io/python-programming/ | Introduction to Python Programming-24ESE1107 | Students could test and debug the programs through mobile itself. |
- Industry Oriented Sessions
Industry Sessions: Session from industry expert was given to students of first year in 2024 curriculum for the subject Basics of Computer Science Engineering
Topic: Design Tools, Figma,Sketch,Adobe XD
Resource Person: Mr. Abhijith V,UI/UX Design and Developer, Ceymox® | Magento
Course: Basics of Computer Science Engineering-24EST1004-K
Faculty Name: Justin Mathew,Gokulnath G
- Gamification in Learning
Course: 20CST206 Operating Systems
An online multilevel game was deployed and students were directed to complete the challenges.
Students completing the challenge was given a certificate for the same.
Faculty Name : Justin Mathew
Website: https://jmat-dev.github.io/oschallenge/
- Flipped Classroom
- Learning strategy: Flipped mode
- Faculty Name: Gokulnath G
- Course: C programming
- Outcome: students could understand the concepts of arrays and utilize arrays in sorting and searching algorithms
- Video of Bubble Sort was shared through LMS platform Linways and discussion on the topic along with quiz was conducted during class hours
Course: Programming Paradigms
Topic: Basic Elements of Prolog
Semester: S8 CSE
Faculty: Ria Mathews
Course: 20 CST402 Distributed Computing
Topic: Maekawa’s Algorithm
Semester: S8 CSE
Faculty: Jinu Thomas
- Usage of ICT tools/Coding platform
- HackerRank – 20CSL204 Operating Sytems Lab – Used for increasing Linux Shell Scripting Proficiency -Thomas Joseph
- Infosys Springboard – Infosys Spring Board Courses were given to students to increase Java Proficiency for Object Oriented Programming Lab. -Thomas Joseph and Jerrin Sebastian
- LeetCode – Assignments deployed on LeetCode was given to students – Jo Cheriyan
Figure 1LeetCode Student Profile
- Case Based Learning as Assignment
Faculty Name: Dr. Anju Pratap
- Course Code-20CST306
- Course Name- Algorithm Analysis and Design
- Batch- 2021 CS-B
Case Study: “Optimizing Delivery Routes”
Scenario: Imagine you are part of a logistics company tasked with optimizing delivery routes for a fleet of delivery trucks. The goal is to minimize fuel consumption and travel time while ensuring that all delivery destinations are met. You are given a list of locations and need to determine the most efficient routes for the trucks to follow.
Problem Breakdown:
Tractable Problem: Suppose the number of delivery points is small—say 10 or 20—and you have access to an efficient algorithm to calculate the best route (e.g., using dynamic programming or greedy algorithms).
Question: How can you design an algorithm that efficiently solves this problem for a small number of delivery points? What algorithms might be applicable, and why is this a tractable problem? [5 marks]
Intractable Problem: Now, imagine the number of delivery points increases to hundreds or thousands, and the problem becomes significantly more complex. Finding the optimal route might involve checking every possible combination, which is computationally expensive (e.g., exponential time complexity).
Question: How would the problem change when the number of delivery points increases dramatically? What makes this problem intractable at scale, and what approximations or heuristics might be used to handle the larger problem?[5 marks]
QUESTIONS
1.Case Study: “Optimizing Delivery Routes”
Scenario: Imagine you are part of a logistics company tasked with optimizing delivery routes for a fleet of delivery trucks. The goal is to minimize fuel consumption and travel time while ensuring that all delivery destinations are met. You are given a list of locations and need to determine the most efficient routes for the trucks to follow.
Problem Breakdown:
Tractable Problem: Suppose the number of delivery points is small—say 10 or 20—and you have access to an efficient algorithm to calculate the best route (e.g., using dynamic programming or greedy algorithms).
Question: How can you design an algorithm that efficiently solves this problem for a small number of delivery points? What algorithms might be applicable, and why is this a tractable problem? [5 marks]
Intractable Problem: Now, imagine the number of delivery points increases to hundreds or thousands, and the problem becomes significantly more complex. Finding the optimal route might involve checking every possible combination, which is computationally expensive (e.g., exponential time complexity).
Question: How would the problem change when the number of delivery points increases dramatically? What makes this problem intractable at scale, and what approximations or heuristics might be used to handle the larger problem? [5 marks]
- YouTube Video Lectures
Figure 2 C Programming Video Lecture
Figure 3 Safad Ismail Youtube Channel
- E Classroom/Doc Share -Slideshare
- Education Blog
- Peer Learning
Topic: How to implement a stack using Queue and Vice Versa.
Subject: Database Management Systems
Faculty: Sheeba Babu
Students gave a positive response on the collaborative learning and many students appreciate the opportunity to interact with their peers in a collaborative learning environment. They also appreciate the chance to learn from their peers who have different backgrounds, experiences, and ways of thinking. I noticed very few students who did not show any interest to share or think. Maybe they didn’t know much about the topics much. I helped such students to join with their groups and also informed others to engage them. They are given chance to speak and think. Thus, the whole class became actively engaged in the collaborative learning.
| S4 CSE A | ||
| PEER GROUP | ||
| Sl_no | Roll Nos | Peer Group |
| 1 | 1 to 5 | 1 |
| 2 | 6 to 11 | 2 |
| 3 | 12 to 16 | 3 |
| 4 | 17 to 21 | 4 |
| 5 | 22 to 26 | 5 |
| 6 | 27 to 31 | 6 |
| 7 | 32 to 36 | 7 |
| 8 | 37 to 41 | 8 |
| 9 | 43 to 47 | 9 |
| 10 | 48 to 52 | 10 |
| 11 | 53 to 57 | 11 |
| 12 | 58 to 62 | 12 |
| 13 | 63 to 67 | 13 |
| 14 | 68 to 72 | 14 |
Course: Computer Graphics and Image Processing
Faculty: Soumya Sara Koshy
- Think-Pair-Share
Topic: Piecewise Linear Transformations
- Think: Students individually analyze a given low-contrast image and reflect on how piecewise linear transformations (such as contrast stretching and gray-level slicing) can enhance its visibility.
- Pair: They discussed their observations with a partner, sharing ideas on how different transformation parameters affect image quality.
- Share: Each pair presents their conclusions to the class, highlighting practical use cases such as medical image enhancement and satellite image processing.
Outcome
This approach encourages critical thinking, peer interaction, and a deeper understanding of piecewise linear transformations in image processing.
- Peer Learning and Collaborative Teaching
Topic: Edge Detection Techniques in Image Processing
Peer Learning
- Students divided into groups, with each group researching a different edge detection method.
- They presented their findings to peers, promoting knowledge exchange.
Collaborative Teaching
- Students co-teached a session, demonstrating implementation in Python (using OpenCV).
- They compared the performance of different techniques based on clarity, noise sensitivity, and computational efficiency.
Outcome
- Developed a deep understanding of various edge detection techniques such as Sobel, Canny, Prewitt, and Roberts.
- Enhanced their research and presentation skills by gathering and presenting information about edge detection methods.
- Collaborated effectively in groups, sharing knowledge and insights on the application of edge detection in image processing.
- Demonstrated practical implementation of edge detection techniques using Python and OpenCV.
- Critically evaluated the strengths and weaknesses of different edge detection algorithms based on clarity, noise sensitivity, and computational efficiency.
- Fostered a collaborative learning environment by teaching and learning from their peers, reinforcing their understanding through teaching others.
- Improved their problem-solving skills by applying edge detection methods to various real-world images.
13. One-Minute Paper
Topic: Gray level transformations
To reinforce the concepts learned in gray level transformations, students participated in a One-Minute Paper activity at the end of the session. They were asked to reflect and write brief responses to the following questions:
- What was the most important concept you learned about gray level transformations today?
- What question do you still have about gray level transformations?
- How do you think gray level transformations are applied in real-world image processing?
Outcome:
This activity helped assess students’ immediate understanding, clarify doubts, and encourage deeper thinking about the topic. The responses were used to guide further discussions and ensure comprehension of key transformation techniques like log transformations, power-law transformations, and contrast stretching.
- Inquiry Based Learning
Topic: Histogram equalization
- Began with a real-world problem statement: Show a low-contrast medical or satellite image and asked, “How can we improve its clarity?”
- Asked students to brainstorm possible methods and record their thoughts.
Student-Led Questioning (Peer Inquiry)
- After explaining the basics, divided students into small groups.
- Each group formulated 3-5 critical questions related to histogram equalization.
- Examples:
- Why does histogram equalization sometimes create artifacts?
- How does histogram equalization affect color images?
- What are the limitations of histogram equalization in medical imaging?
- Groups swapped questions with another team and try to answer them.
- Discussed answers as a class.
Outcome:
- Students gained a solid understanding of histogram equalization, including its effects on image contrast and its application in different domains such as medical imaging and satellite image processing.
- Group discussions and peer inquiry promoted teamwork, active participation, and diverse perspectives, leading to a more enriched learning experience.
- Concept Quiz
- Used Google Forms for a quick quiz on histograms.
- Example questions:
- What is the primary goal of histogram equalization?
- What kind of images does histogram equalization work best with?
- What is the effect of applying histogram equalization to a grayscale image?
- Project Based Learning
Faculty: Dr. Shibu K R
Course: Operating Systems
A Brainstorming session was conduced for the course and the possibility to do project from the concepts in Operating Systems was sought from students. Various ideas emerged and web based simulation projects were the most recommended. Based on the findings and discussions students were directed to develop web based solution for the concepts.
Akshay Joseph, of CSE-A, roll number-17 developed a website to illustrate the working of Dining Philosophers problem Link is given below below.
https://akshayajoseph.github.io/dining_philosopher_problem/
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GitHub Repos