Deeper Learning: a case study from a data analysis course #58
Quote of the Week
“Intellectual activity, begins with someone asking a question. That is, someone wondering, puzzled, confused. School books, textbooks, rarely help us to see this. They tell us right answers, but very rarely the questions that first led people to look for those answers.” – John Holt, Educator and Author, Instead of Education: Ways to Help People Do Things Better: Way to Help People Do Things Better
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Deeper Learning
In this post, Sapna Shah, Academic Dean at the GenWise Summer Program running at Manipal University currently, talks about the importance of ‘deeper learning’ and shares an example of this she observed in a 1-week course on data analysis.
In their book, ‘In Search for Deeper Learning’ Jal Mehta and Sarah Fine say that Deeper Learning emerges at the intersection of mastery, identity and creativity.
Mastery is developing significant knowledge and skill;
Identity is seeing yourself as connected to doing the work;
Creativity is not just taking in knowledge but doing something in the field.
Research suggests that successfully navigating the 21st century adult life will require far more than basic academic knowledge and skills across a range of domains.
On the personal front, adults need to be able to navigate among plural identities, confront complex ethical questions, and make informed decisions in the face of uncertainty (Kegan 2003).
On the civic front, they need to be able to articulate and advocate for their perspectives, to engage in productive dialogue across ideological divides, and to decide among imperfect options (Levinson 2012).
On the professional front, they need to be able tackle open ended problems in critical, creative, and collaborative ways (Murnane & Levy 1996; Trilling & Fadel 2009), and to engage in ongoing learning that allows them to adapt to the needs of a rapidly changing job market (Wagner 2008).
Hence it is critical to create learning experiences that are deep, powerful and empowering for all students.
Deeper learning is a hallmark of GenWise Summer Camps. Students have agency in selecting the courses they want to pursue. Our instructors have authentic relationships with the work of their disciplines. Their deep content knowledge and expertise allows them to facilitate in-depth, intellectual explorations of their subject and nurture student curiosity. All of them have a sharp vision for what they really want students to come out of their classes with.
Let’s get a glimpse of one such classroom.
Welcome to Dr. Ashish Kulkarni’s Data Science class –
Students are learning about hypothesis testing.
The Case: Rutuja Diwekar, a well known dietician, has hired the students as statistician consultants to evaluate the results of a new diet she has developed. Her hypothesis is that the diet will help clients reduce their triglyceride levels and body weight. They have conducted a study with 16 clients and now they have to use the data set provided to evaluate whether this is a valid or null hypothesis.
There is a buzz in the classroom as students start discussing the case.
Students compare the average body weight of all clients at the start of the diet with the average body weight of all clients at the end of the diet and notice a 5% reduction in weight. They also compare the average triglyceride levels of all clients at the start of the diet with the average triglyceride levels of all clients at the end of the diet and notice a 10% reduction.
Based on these results they conclude that Ms. Diwekar’s hypothesis about the diet is valid.
However this is too soon and too simple a response for Ashish. He goes to ask the students, “What is the chance of them being wrong when they reach the conclusion?”
It is not surprising that this is followed by a hushed silence in the room.
Students take their time to make sense of the question. They go back to the drawing board to review the different numbers. One realisation that hits them soon is that even though on average the triglyceride levels have reduced by 10% there is a lot of noise in the data. In other words, reduction in triglyceride levels was not consistently observed for all participants of the study.
Contrary to this, even though the average reduction in weight is only 5% they notice that the data is consistent. In other words the signal is strong. Eventually students revise their conclusion that Ms. Diwekar’s hypothesis about the diet is not valid.
This is an aha moment for them! As statisticians they can’t take things at face value. They have to be thinking about the signal and the noise.
Ashish’s class and instructions lie at the intersection of mastery, identity, creativity. It goes beyond factual recall and procedural fluency. Students are working as statisticians and applying the knowledge and skills they learned in class to a case study. This kind of learning is rare but surely the need of the hour in this rapidly changing world of ours.
If you are interested in our upcoming residential summer program at Shiv Nadar University (NCR) from June 16- 26, 2022, please check out this post.