Why experiential learning is a ‘necessity’ and not a ‘luxury’ in Data Science courses?

I hear and I forget, I see and I remember, I do and I understand. ~ Confucius, 450 BC.

Some thousands of years after, the relevance of this quote remains intact.

Northern Illinois University has recently stated that “Unlike traditional classroom situations where students may compete with one another or remain uninvolved or unmotivated and where the instruction is highly structured, students in experiential learning situations cooperate and learn from one another in a more semi-structured approach. Instruction is designed to engage students in direct experiences that are tied to real-world problems and situations in which the instructor facilitates rather than directs student progress.”

That said, experiential or hands-on learning is not a new concept. Renowned educational psychologists such as John Dewey, Carl Rogers, and David Kolb have provided substantial reports of learning theories that focus on “learning through experience" or “learning by doing.”

Today, in the 21-st century, students might find it challenging to connect with in-class learning without having outside experiences. Acquiring experiential learning required more than ‘monkey sees, monkey does’, i.e. the experiential learning approach encourages both individual learnings along with the collective and critical reflection. While learning data science, experiential learning focuses on the problems that are to be solved instead of specific methods. Also, students get experience with real-world problems while searching for solutions to an actual experience, which also leads to an increase in creative and decision-making skills.

How theoretical learning is passe?

Being somebody with just theoretical knowledge can be superannuated in this competitive world. Via case studies, presentations, assignments, and assessments, the students can have both hands-on and theoretical aspects of data science. Through experiential learning, the students can primarily focus on getting acquainted with real-time case studies based on the concepts of data science.

The high demand for experiential learning for data science has prompted many institutes to include experiential learning in their data science curriculum along with theoretical knowledge. Also, experiential learning will act as a gateway for students willing to explore their career paths in data science and help them gain relevant work experience with real life-life scenarios.

Experiential learning will help students to prepare to work in a real job and shape them for relevant data scientist jobs using ambiguity, realism, and complexity. As a data scientist, the students will face situations where the information provided will not be complete and certain. In such cases, students will be required to use deep critical thinking in as many ways and learn to navigate towards results with ambiguity.

Projects in experiential learning are designed in such a way that the outcomes are specific and realistic, unlike the ones in traditional learning that are specific but not realistic. The cases are designed based on real business problems, leaving space for students to figure out the necessary outcome, not just a specific answer.

The complexity of the projects leads students direct to develop critical thinking and decision-making ability. As in the workplace, you don’t always know what decision you are going to make or what outcomes a situation holds. The degree of complexity will help students to lead the project and build an outcome over time using ambiguity and realistic decision making.

Students build these skills over time to become a data scientist with experiential learning that can face any situation at the workplace with skills and hands-on experience to apply the required skills at work. Experiential learning does not feel unsafe as in the end students evolve as a successful and experienced data scientist.

Experiential Learning with LEARNXT

Experiential learning is becoming a common part of educational institutes across the globe. SRM Group has unveiled a digital learning brand (LEARNXT) which aims at empowering people to achieve their dreams through education. LEARNXT is a great place where students can learn more and be more with experiential learning. Quoting Sridhar Nagarajachar (CEO, LEARNXT, SRM Group), ‘throughout our programme, we empower professionals to learn skills that enable them to evolve as "T shaped" and "Pi shaped" people’. LEARNXT focuses on who, what, and how of teaching and learning experience. Let’s have a look at how experiential learning with LEARNXT is a top-drawer:

  • Students will get access to the relevant content as per the future and hands-on real life-like experience with experiential learning.
  • Learning will be a blend of assignments, assessments, labs, tools, and hands-on realistic project solving. All of these learning will be engineered with StudyNxt and 360 degree career support through CareerNxt, our world-class career services and placement assistant platform.
  • With LEARNXT and experiential learning, students will get the opportunity to apply data science in real-world situations. There are multiple solutions to a challenge, and through experiential learning, students will learn to apply data to seek unique solutions for business.
  • Students will also get to analyse how their actions affected the issue and how their outcome varies from other students’. Varied circumstances, you will learn how the concepts are applied to various situations.

At some point in time, you will also realise that some approaches work better than others while working your brains on hands-on tasks. A mistake, disbarred action, discarded methods, and abandoned ideas will become a valuable part of your learning process. Valuing mistakes will bring true wisdom to experiential learning. Experiential learning in data science and LEARNXT will eventually help the students to enhance their knowledge and skills. Students can experience great delight in learning by laying an active role in real-life projects. We will design projects that correspond to real-life problems and leave students with the space to figure out outcomes and solutions to the problem on their own.