Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)

MIT OpenCourseWare · Beginner ·📊 Data Analytics & Business Intelligence ·6y ago

Key Takeaways

Dr. Anjali Sastry discusses social impact at scale using data analytics, describing her approach to redesigning traditional projects for greater effectiveness.

Full Transcript

Part of our challenge as teachers is to help make sure that we're embedding into their approach enough rigor, that we're looking at the data and the evidence, and that it's being linked to the content we're teaching here. Today on the podcast, we're looking at a new kind of independent study. Teaching this way is incredibly rewarding and also really scary. You'll often be invited into domains where you don't have the expertise, and it's quite hard to predict how a given session or conversation will go. Welcome to Chalk Radio, a podcast about inspired teaching at MIT. I'm your host, Sarah Hansen, from MIT Open Courseware. In this episode, we'll be talking about action learning and an exciting evolution of the independent study. The MIT Sloan School of Management has a tradition of involving MBA fellows in collaborations with partners from around the globe. For years, these projects have helped participants build experience and connections, and these students, the MBA fellows, are often already established in a career or in building their own projects. My guest today, Dr. Anjali Sastry, has led over 100 groups of MBA fellows in these projects. Now, the thing to note here is that these projects were developed by the school and instructors, and students would sign on to work on them. But Dr. Sastry wanted to get students more invested right from the start. So, she decided to do something a little different. We'll pick up with Dr. Sastry's explanation. This last year, I tried a new experiment where, instead of us bringing in the projects, I I students work with me to create the projects they were interested in that spoke to societal challenges that would be important in the coming decade and that also linked to technology in some way. Students come in with a passion or a set of questions they're really interested in investigating. Could we take some of the things that work in action learning traditional action learning classes and bend them towards that kind of drive? Could I use my own connections, relationships, Rolodex to help students follow their passions but also to find thought partners or project partners in the world? Uh and also tap into my own expertise and that of other MIT faculty as well as their peers. Could you talk a little bit about some of the specific projects that students did? The projects students did ranged greatly but all had as a theme using what they were learning here and tapping into emerging or existing technology in new ways to find solutions to problems that face many. One of my students, Alini, came to MIT with completely on fire with this idea of tapping into analytics and AI to solve what she saw as a major challenge when it came to bringing finance to small holder and small-scale farmers. She's an expert on agricultural finance and she realized that in her native Brazil and elsewhere people who didn't have good credit standing and good credit records, um farmers who didn't have those assets couldn't get loans and were forever locked out. So, there's a big dichotomy between the people have access to finance and those who don't. Could she break down that barrier in some way? Could new forms of data and technology help do that? So, instead of relying only on somebody's credit record, could you look at the weather, their farming choices, their behaviors, and physical data, and use it to come up with a better assessment of the riskiness of a given farmer from the lenders' point of view? Slightly technical area, but it actually has huge implications because it could open the door to all kinds of folks who are excluded from traditional finance by providing other methods of sorting out their riskiness and their creditworthiness. So, she's got drone data, map and weather data, behavioral data, you know, transactional data, and is trying to stitch it all up in new ways. Each of these projects required significant time and engagement from Dr. Sastry. I couldn't help but ask how this all worked. What did it look like to be involved with each student's project? Each project was customized, and one of the things I've learned about trying to innovate when it comes to teaching is you have to be willing to invest a lot the first few rounds, the first few years you do something, and then over time you learn how to make it a little more efficient, streamlined, and maybe scalable, and more cost-effective. I learned I had to keep a whiteboard up in my office with each project and each person cuz I had over a dozen running to keep them straight and try to remember who's doing what so that when I saw it when I came into the office, that person would be on my mind or that project would be on my mind. I do think, mundane as it sounds, I think it's also important to have good like forms and paperwork. Having people really articulate in writing briefly what they want to do and having a structured kind of update process really helps keep the momentum and build the focus. How do you measure success in projects like this? It's a great question because students who are following their passions really get into it. You give them enough freedom they will have a good time. But part of our challenge as their teachers is to help make sure that we're embedding into their approach enough rigor that we're looking at the data and the evidence and that it's being linked to the content we're teaching here. So a passion project by itself may or may not be academic. Not every student who comes in with their passion project is necessarily going to want to or should turn it into an in-depth study. So I've learned that you can't convert everyone and that's okay because we're offering it this is an offering of a new learning experience. It's very much driven by the students' interest. You need to really be able to allow the students to self-select in. It's hard to offer something like this as a requirement. So that's another piece is striking the right balance between kind of opening the door and inviting people in versus cracking the whip and following up on them. Dr. Sastry pointed to one of the challenges that she faced in taking on these students' projects and it's one that many instructors face at some point. How far would she go outside her area of expertise to support her students? I need to be able to figure it out for myself and then also be very clear with the students. How much do I know about the blockchain? That's not an area of my research. So, how far do I want to go down a set of projects that take that on? I'm interested in this topic. I'll learn with you, but my domain of expertise relates to these areas. We can apply it to your questions. We can apply systems thinking or organizational change or business models to the questions you're articulating, but I am not the technical expert on this domain, so you'll need to work with someone else on that. And different instructors have different interests in stepping out of their comfort zone, so that I think that's really important. Um another big question is how willing are you as an instructor to have a mishmash of projects that take on very different domains. Teaching this way is incredibly rewarding and also really scary. You'll often be invited into domains where you don't have the expertise and it's quite hard to predict how a given session or conversation will go. It's not like running a case where you know the story and you know what you're going to say at each moment in class um or you at least got a sense of what that might look like because very often students will come in and say, "I've totally changed my mind." And you have to deal with that. So, there's both the kind of personal journey of learning that's less predictable and the domain part that's less predictable, but you as the instructor get to set some of those parameters. So, I want them to not simply kind of make general arguments about what should happen, but to think about what they could do as a leader, an entrepreneur, and executive. If you're interested in learning more about social impact technology projects or independent studies, you can find Dr. Sastry's teaching materials on our site at ocw.mit.edu. You'll also find videos of Dr. Sastry's students sharing their insights about their own projects. Additional materials and instructor insights about project-based learning are on our educator portal at ocw.mit.edu/educator. As always, thank you so much for listening. We'd love for your OCW story to become a part of our conversation. Use the contact link in the show notes to share how you use OCW to shape your own life or those of others. Your story will inspire us and other people, and will help us continue to share the kind of open educational resources that make a difference. Until next time, I'm Sarah Hansen from MIT Open Courseware.

Original Description

MIT Chalk Radio, Season 1 Instructor: Anjali Sastry, Sarah Hansen Subscribe here → https://chalk-radio.simplecast.com/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63YwKIMA9K08FFvdeBEl6Lo *Description* In this episode, Dr. Anjali Sastry describes how she redesigned a traditional independent study to encourage MBA fellows to find social impact solutions that scale. *Episode Notes* This episode explores a new kind of independent study. MIT has traditionally encouraged its Sloan MBA fellows to engage in international projects with partners around the globe. Our guest, Dr. Anjali Sastry, has led over 100 groups of MBA fellows in these projects. But she recently changed the structure of the class so that instead of signing on to projects developed by instructors, students are now able to develop their own projects based on their own interests. All the new projects in this course called 15.960 New Executive Thinking Social-Impact Projects involve applying technology in new ways to find solutions to common problems worldwide. In one project, for example, a student employed data analytics to improve financing prospects for small-scale farmers in Brazil and elsewhere. Sastry finds that mentoring a variety of students with disparate interests presents a real challenge, because it often involves working in areas beyond her own area of expertise. And keeping track of the various projects required her to develop a very structured process for students to use in reporting their progress. It’s worth the extra effort, though it isn’t easy, says Sastry. “Teaching this way is incredibly rewarding, and also really scary.” Relevant Resources: MIT OpenCourseWare https://ocw.mit.edu/index.htm The OCW Educator Portal https://ocw.mit.edu/educator/ Dr. Sastry’s Social-Impact course on OCW https://ocw.mit.edu/courses/sloan-school-of-management/15-960-new-executive-thinking-social-impact-technology-projects-fall-2017-spring-2018/ Other courses by Dr. Sastry on OC
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Playlist

Uploads from MIT OpenCourseWare · MIT OpenCourseWare · 11 of 60

1 21. Post Trade Clearing, Settlement & Processing
21. Post Trade Clearing, Settlement & Processing
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2 10. Financial System Challenges & Opportunities
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3 7. Technical Challenges
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4 3. Blockchain Basics & Cryptography
3. Blockchain Basics & Cryptography
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5 19. Primary Markets, ICOs & Venture Capital, Part 1
19. Primary Markets, ICOs & Venture Capital, Part 1
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6 1. Introduction for 15.S12 Blockchain and Money, Fall 2018
1. Introduction for 15.S12 Blockchain and Money, Fall 2018
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7 Chalk Radio, A Podcast about Inspired Teaching at MIT (Teaser)
Chalk Radio, A Podcast about Inspired Teaching at MIT (Teaser)
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8 Nuclear Gets Personal with Prof. Michael Short (S1:E1)
Nuclear Gets Personal with Prof. Michael Short (S1:E1)
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9 How Africa Has Been Made to Mean with Prof. Amah Edoh (S1:E2)
How Africa Has Been Made to Mean with Prof. Amah Edoh (S1:E2)
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10 Making Deep Learning Human with Prof. Gilbert Strang (S1:E3)
Making Deep Learning Human with Prof. Gilbert Strang (S1:E3)
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▶ Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)
Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)
MIT OpenCourseWare
12 Film is for Everyone with Prof. David Thorburn (S1:E5)
Film is for Everyone with Prof. David Thorburn (S1:E5)
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13 Lecture 12: Aircraft Performance
Lecture 12: Aircraft Performance
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14 Lecture 3: Learning to Fly
Lecture 3: Learning to Fly
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15 Lecture 13:  Interpreting Weather Data
Lecture 13: Interpreting Weather Data
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16 Lecture 21: Weather Minimums and Final Tips
Lecture 21: Weather Minimums and Final Tips
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17 Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
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18 Part 4: Eigenvalues and Eigenvectors
Part 4: Eigenvalues and Eigenvectors
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19 Part 5: Singular Values and Singular Vectors
Part 5: Singular Values and Singular Vectors
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20 Part 3: Orthogonal Vectors
Part 3: Orthogonal Vectors
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21 Part 2: The Big Picture of Linear Algebra
Part 2: The Big Picture of Linear Algebra
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22 Part 1: The Column Space of a Matrix
Part 1: The Column Space of a Matrix
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23 Intro: A New Way to Start Linear Algebra
Intro: A New Way to Start Linear Algebra
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24 9. Chromatin Remodeling and Splicing
9. Chromatin Remodeling and Splicing
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25 28. Visualizing Life - Fluorescent Proteins
28. Visualizing Life - Fluorescent Proteins
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26 20. Roth's theorem III: polynomial method and arithmetic regularity
20. Roth's theorem III: polynomial method and arithmetic regularity
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27 8. Szemerédi's graph regularity lemma III: further applications
8. Szemerédi's graph regularity lemma III: further applications
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28 19. Roth's theorem II: Fourier analytic proof in the integers
19. Roth's theorem II: Fourier analytic proof in the integers
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29 12. Pseudorandom graphs II: second eigenvalue
12. Pseudorandom graphs II: second eigenvalue
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30 1. A bridge between graph theory and additive combinatorics
1. A bridge between graph theory and additive combinatorics
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31 Special Episode: Teaching Remotely During Covid-19 with Prof. Justin Reich
Special Episode: Teaching Remotely During Covid-19 with Prof. Justin Reich
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32 Spring 2020 Update from Dean Rajagopal
Spring 2020 Update from Dean Rajagopal
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33 S1E7: Unpacking Misconceptions about Language & Identities with Prof. Michel DeGraff
S1E7: Unpacking Misconceptions about Language & Identities with Prof. Michel DeGraff
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34 Climate 101 Live
Climate 101 Live
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35 Welcome for Volunteers (for EarthDNA's Climate 101)
Welcome for Volunteers (for EarthDNA's Climate 101)
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36 Learning to Fly with Drs. Philip Greenspun & Tina Srivastava (S1:E8)
Learning to Fly with Drs. Philip Greenspun & Tina Srivastava (S1:E8)
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37 Thinking Like an Economist with Prof. Jonathan Gruber (S1:E9)
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38 2. Cyber Network Data Processing; AI Data Architecture
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39 1. Artificial Intelligence and Machine Learning
1. Artificial Intelligence and Machine Learning
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40 2: Resistor Capacitor Circuit and Nernst Potential - Intro to Neural Computation
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41 14: Rate Models and Perceptrons - Intro to Neural Computation
14: Rate Models and Perceptrons - Intro to Neural Computation
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42 4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
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43 18: Recurrent Networks - Intro to Neural Computation
18: Recurrent Networks - Intro to Neural Computation
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44 3: Resistor Capacitor Neuron Model - Intro to Neural Computation
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45 15: Matrix Operations - Intro to Neural Computation
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46 13: Spectral Analysis Part 3 - Intro to Neural Computation
13: Spectral Analysis Part 3 - Intro to Neural Computation
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47 16: Basis Sets - Intro to Neural Computation
16: Basis Sets - Intro to Neural Computation
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48 20: Hopfield Networks - Intro to Neural Computation
20: Hopfield Networks - Intro to Neural Computation
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49 8: Spike Trains - Intro to Neural Computation
8: Spike Trains - Intro to Neural Computation
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50 7: Synapses - Intro to Neural Computation
7: Synapses - Intro to Neural Computation
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51 19: Neural Integrators - Intro to Neural Computation
19: Neural Integrators - Intro to Neural Computation
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52 5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
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53 6: Dendrites - Intro to Neural Computation
6: Dendrites - Intro to Neural Computation
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54 17: Principal Components Analysis_ - Intro to Neural Computation
17: Principal Components Analysis_ - Intro to Neural Computation
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55 12: Spectral Analysis Part 2 - Intro to Neural Computation
12: Spectral Analysis Part 2 - Intro to Neural Computation
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56 11: Spectral Analysis Part 1 - Intro to Neural Computation
11: Spectral Analysis Part 1 - Intro to Neural Computation
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57 9: Receptive Fields - Intro to Neural Computation
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58 10: Time Series - Intro to Neural Computation
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59 1: Course Overview and Ionic Currents - Intro to Neural Computation
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60 The Power of OER with Profs. Mary Rowe and Elizabeth Siler (S1:E10)
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Dr. Anjali Sastry shares her approach to using data analytics for social impact at scale, providing insights into redesigning traditional projects for greater effectiveness. This episode is part of the MIT Chalk Radio series, exploring the intersection of data analytics and social impact. By applying data-driven decision making, listeners can create more effective social impact projects.

Key Takeaways
  1. Identify key metrics for social impact assessment
  2. Redesign traditional projects using data analytics
  3. Apply data-driven decision making to social impact projects
  4. Collaborate with stakeholders to implement project changes
  5. Monitor and evaluate project effectiveness using data analytics
💡 Data analytics can be a powerful tool for social impact, enabling organizations to redesign traditional projects for greater effectiveness and create more meaningful change.

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