Education, teaching, academics

Subterms

    Latest story

    125 Shares148 Views

    “They can see themselves shaping the world they live in”

    by

    During the journey from the suburbs to the city, the tree canopy often dwindles down as skyscrapers rise up. A group of New England Innovation Academy students wondered why that is.“Our friend Victoria noticed that where we live in Marlborough there are lots of trees in our own backyards. But if you drive just 30 minutes to Boston, there are almost no trees,” said high school junior Ileana Fournier. “We were struck by that duality.”This inspired Fournier and her classmates Victoria Leeth and Jessie Magenyi to prototype a mobile app that illustrates Massachusetts deforestation trends for Day of AI, a free, hands-on curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative, headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. They were among a group of 20 students from New England Innovation Academy who shared their projects during the 2024 Day of AI global celebration hosted with the Museum of Science.The Day of AI curriculum introduces K-12 students to artificial intelligence. Now in its third year, Day of AI enables students to improve their communities and collaborate on larger global challenges using AI. Fournier, Leeth, and Magenyi’s TreeSavers app falls under the Telling Climate Stories with Data module, one of four new climate-change-focused lessons.“We want you to be able to express yourselves creatively to use AI to solve problems with critical-thinking skills,” Cynthia Breazeal, director of MIT RAISE, dean for digital learning at MIT Open Learning, and professor of media arts and sciences, said during this year’s Day of AI global celebration at the Museum of Science. “We want you to have an ethical and responsible way to think about this really powerful, cool, and exciting technology.”Moving from understanding to actionDay of AI invites students to examine the intersection of AI and various disciplines, such as history, civics, computer science, math, and climate change. With the curriculum available year-round, more than 10,000 educators across 114 countries have brought Day of AI activities to their classrooms and homes.The curriculum gives students the agency to evaluate local issues and invent meaningful solutions. “We’re thinking about how to create tools that will allow kids to have direct access to data and have a personal connection that intersects with their lived experiences,” Robert Parks, curriculum developer at MIT RAISE, said at the Day of AI global celebration.Before this year, first-year Jeremie Kwapong said he knew very little about AI. “I was very intrigued,” he said. “I started to experiment with ChatGPT to see how it reacts. How close can I get this to human emotion? What is AI’s knowledge compared to a human’s knowledge?”In addition to helping students spark an interest in AI literacy, teachers around the world have told MIT RAISE that they want to use data science lessons to engage students in conversations about climate change. Therefore, Day of AI’s new hands-on projects use weather and climate change to show students why it’s important to develop a critical understanding of dataset design and collection when observing the world around them.“There is a lag between cause and effect in everyday lives,” said Parks. “Our goal is to demystify that, and allow kids to access data so they can see a long view of things.”Tools like MIT App Inventor — which allows anyone to create a mobile application — help students make sense of what they can learn from data. Fournier, Leeth, and Magenyi programmed TreeSavers in App Inventor to chart regional deforestation rates across Massachusetts, identify ongoing trends through statistical models, and predict environmental impact. The students put that “long view” of climate change into practice when developing TreeSavers’ interactive maps. Users can toggle between Massachusetts’s current tree cover, historical data, and future high-risk areas.Although AI provides fast answers, it doesn’t necessarily offer equitable solutions, said David Sittenfeld, director of the Center for the Environment at the Museum of Science. The Day of AI curriculum asks students to make decisions on sourcing data, ensuring unbiased data, and thinking responsibly about how findings could be used.“There’s an ethical concern about tracking people’s data,” said Ethan Jorda, a New England Innovation Academy student. His group used open-source data to program an app that helps users track and reduce their carbon footprint.Christine Cunningham, senior vice president of STEM Learning at the Museum of Science, believes students are prepared to use AI responsibly to make the world a better place. “They can see themselves shaping the world they live in,” said Cunningham. “Moving through from understanding to action, kids will never look at a bridge or a piece of plastic lying on the ground in the same way again.”Deepening collaboration on earth and beyondThe 2024 Day of AI speakers emphasized collaborative problem solving at the local, national, and global levels.“Through different ideas and different perspectives, we’re going to get better solutions,” said Cunningham. “How do we start young enough that every child has a chance to both understand the world around them but also to move toward shaping the future?”Presenters from MIT, the Museum of Science, and NASA approached this question with a common goal — expanding STEM education to learners of all ages and backgrounds.“We have been delighted to collaborate with the MIT RAISE team to bring this year’s Day of AI celebration to the Museum of Science,” says Meg Rosenburg, manager of operations at the Museum of Science Centers for Public Science Learning. “This opportunity to highlight the new climate modules for the curriculum not only perfectly aligns with the museum’s goals to focus on climate and active hope throughout our Year of the Earthshot initiative, but it has also allowed us to bring our teams together and grow a relationship that we are very excited to build upon in the future.”Rachel Connolly, systems integration and analysis lead for NASA’s Science Activation Program, showed the power of collaboration with the example of how human comprehension of Saturn’s appearance has evolved. From Galileo’s early telescope to the Cassini space probe, modern imaging of Saturn represents 400 years of science, technology, and math working together to further knowledge.“Technologies, and the engineers who built them, advance the questions we’re able to ask and therefore what we’re able to understand,” said Connolly, research scientist at MIT Media Lab.New England Innovation Academy students saw an opportunity for collaboration a little closer to home. Emmett Buck-Thompson, Jeff Cheng, and Max Hunt envisioned a social media app to connect volunteers with local charities. Their project was inspired by Buck-Thompson’s father’s difficulties finding volunteering opportunities, Hunt’s role as the president of the school’s Community Impact Club, and Cheng’s aspiration to reduce screen time for social media users. Using MIT App Inventor, ​their combined ideas led to a prototype with the potential to make a real-world impact in their community.The Day of AI curriculum teaches the mechanics of AI, ethical considerations and responsible uses, and interdisciplinary applications for different fields. It also empowers students to become creative problem solvers and engaged citizens in their communities and online. From supporting volunteer efforts to encouraging action for the state’s forests to tackling the global challenge of climate change, today’s students are becoming tomorrow’s leaders with Day of AI.“We want to empower you to know that this is a tool you can use to make your community better, to help people around you with this technology,” said Breazeal.Other Day of AI speakers included Tim Ritchie, president of the Museum of Science; Michael Lawrence Evans, program director of the Boston Mayor’s Office of New Urban Mechanics; Dava Newman, director of the MIT Media Lab; and Natalie Lao, executive director of the App Inventor Foundation. More

    More stories

    • 100 Shares199 Views

      in Data Management & Statistics

      Fostering research, careers, and community in materials science

      by

      Gabrielle Wood, a junior at Howard University majoring in chemical engineering, is on a mission to improve the sustainability and life cycles of natural resources and materials. Her work in the Materials Initiative for Comprehensive Research Opportunity (MICRO) program has given her hands-on experience with many different aspects of research, including MATLAB programming, experimental design, data analysis, figure-making, and scientific writing.Wood is also one of 10 undergraduates from 10 universities around the United States to participate in the first MICRO Summit earlier this year. The internship program, developed by the MIT Department of Materials Science and Engineering (DMSE), first launched in fall 2021. Now in its third year, the program continues to grow, providing even more opportunities for non-MIT undergraduate students — including the MICRO Summit and the program’s expansion to include Northwestern University.“I think one of the most valuable aspects of the MICRO program is the ability to do research long term with an experienced professor in materials science and engineering,” says Wood. “My school has limited opportunities for undergraduate research in sustainable polymers, so the MICRO program allowed me to gain valuable experience in this field, which I would not otherwise have.”Like Wood, Griheydi Garcia, a senior chemistry major at Manhattan College, values the exposure to materials science, especially since she is not able to learn as much about it at her home institution.“I learned a lot about crystallography and defects in materials through the MICRO curriculum, especially through videos,” says Garcia. “The research itself is very valuable, as well, because we get to apply what we’ve learned through the videos in the research we do remotely.”Expanding research opportunitiesFrom the beginning, the MICRO program was designed as a fully remote, rigorous education and mentoring program targeted toward students from underserved backgrounds interested in pursuing graduate school in materials science or related fields. Interns are matched with faculty to work on their specific research interests.Jessica Sandland ’99, PhD ’05, principal lecturer in DMSE and co-founder of MICRO, says that research projects for the interns are designed to be work that they can do remotely, such as developing a machine-learning algorithm or a data analysis approach.“It’s important to note that it’s not just about what the program and faculty are bringing to the student interns,” says Sandland, a member of the MIT Digital Learning Lab, a joint program between MIT Open Learning and the Institute’s academic departments. “The students are doing real research and work, and creating things of real value. It’s very much an exchange.”Cécile Chazot PhD ’22, now an assistant professor of materials science and engineering at Northwestern University, had helped to establish MICRO at MIT from the very beginning. Once at Northwestern, she quickly realized that expanding MICRO to Northwestern would offer even more research opportunities to interns than by relying on MIT alone — leveraging the university’s strong materials science and engineering department, as well as offering resources for biomaterials research through Northwestern’s medical school. The program received funding from 3M and officially launched at Northwestern in fall 2023. Approximately half of the MICRO interns are now in the program with MIT and half are with Northwestern. Wood and Garcia both participate in the program via Northwestern.“By expanding to another school, we’ve been able to have interns work with a much broader range of research projects,” says Chazot. “It has become easier for us to place students with faculty and research that match their interests.”Building communityThe MICRO program received a Higher Education Innovation grant from the Abdul Latif Jameel World Education Lab, part of MIT Open Learning, to develop an in-person summit. In January 2024, interns visited MIT for three days of presentations, workshops, and campus tours — including a tour of the MIT.nano building — as well as various community-building activities.“A big part of MICRO is the community,” says Chazot. “A highlight of the summit was just seeing the students come together.”The summit also included panel discussions that allowed interns to gain insights and advice from graduate students and professionals. The graduate panel discussion included MIT graduate students Sam Figueroa (mechanical engineering), Isabella Caruso (DMSE), and Eliana Feygin (DMSE). The career panel was led by Chazot and included Jatin Patil PhD ’23, head of product at SiTration; Maureen Reitman ’90, ScD ’93, group vice president and principal engineer at Exponent; Lucas Caretta PhD ’19, assistant professor of engineering at Brown University; Raquel D’Oyen ’90, who holds a PhD from Northwestern University and is a senior engineer at Raytheon; and Ashley Kaiser MS ’19, PhD ’21, senior process engineer at 6K.Students also had an opportunity to share their work with each other through research presentations. Their presentations covered a wide range of topics, including: developing a computer program to calculate solubility parameters for polymers used in textile manufacturing; performing a life-cycle analysis of a photonic chip and evaluating its environmental impact in comparison to a standard silicon microchip; and applying machine learning algorithms to scanning transmission electron microscopy images of CrSBr, a two-dimensional magnetic material. “The summit was wonderful and the best academic experience I have had as a first-year college student,” says MICRO intern Gabriella La Cour, who is pursuing a major in chemistry and dual degree biomedical engineering at Spelman College and participates in MICRO through MIT. “I got to meet so many students who were all in grades above me … and I learned a little about how to navigate college as an upperclassman.” “I actually have an extremely close friendship with one of the students, and we keep in touch regularly,” adds La Cour. “Professor Chazot gave valuable advice about applications and recommendation letters that will be useful when I apply to REUs [Research Experiences for Undergraduates] and graduate schools.”Looking to the future, MICRO organizers hope to continue to grow the program’s reach.“We would love to see other schools taking on this model,” says Sandland. “There are a lot of opportunities out there. The more departments, research groups, and mentors that get involved with this program, the more impact it can have.” More

    • 75 Shares159 Views

      in Data Management & Statistics

      “We offer another place for knowledge”

      by

      In the Dzaleka Refugee Camp in Malawi, Jospin Hassan didn’t have access to the education opportunities he sought. So, he decided to create his own. 

      Hassan knew the booming fields of data science and artificial intelligence could bring job opportunities to his community and help solve local challenges. After earning a spot in the 2020-21 cohort of the Certificate Program in Computer and Data Science from MIT Refugee Action Hub (ReACT), Hassan started sharing MIT knowledge and skills with other motivated learners in Dzaleka.

      MIT ReACT is now Emerging Talent, part of the Jameel World Education Lab (J-WEL) at MIT Open Learning. Currently serving its fifth cohort of global learners, Emerging Talent’s year-long certificate program incorporates high-quality computer science and data analysis coursework from MITx, professional skill building, experiential learning, apprenticeship work, and opportunities for networking with MIT’s global community of innovators. Hassan’s cohort honed their leadership skills through interactive online workshops with J-WEL and the 10-week online MIT Innovation Leadership Bootcamp. 

      “My biggest takeaway was networking, collaboration, and learning from each other,” Hassan says.

      Today, Hassan’s organization ADAI Circle offers mentorship and education programs for youth and other job seekers in the Dzaleka Refugee Camp. The curriculum encourages hands-on learning and collaboration.

      Launched in 2020, ADAI Circle aims to foster job creation and reduce poverty in Malawi through technology and innovation. In addition to their classes in data science, AI, software development, and hardware design, their Innovation Hub offers internet access to anyone in need. 

      Doing something different in the community

      Hassan first had the idea for his organization in 2018 when he reached a barrier in his own education journey. There were several programs in the Dzaleka Refugee Camp teaching learners how to code websites and mobile apps, but Hassan felt that they were limited in scope. 

      “We had good devices and internet access,” he says, “but I wanted to learn something new.” 

      Teaming up with co-founder Patrick Byamasu, Hassan and Byamasu set their sights on the longevity of AI and how that might create more jobs for people in their community. “The world is changing every day, and data scientists are in a higher demand today in various companies,” Hassan says. “For this reason, I decided to expand and share the knowledge that I acquired with my fellow refugees and the surrounding villages.”

      ADAI Circle draws inspiration from Hassan’s own experience with MIT Emerging Talent coursework, community, and training opportunities. For example, the MIT Bootcamps model is now standard practice for ADAI Circle’s annual hackathon. Hassan first introduced the hackathon to ADAI Circle students as part of his final experiential learning project of the Emerging Talent certificate program. 

      ADAI Circle’s annual hackathon is now an interactive — and effective — way to select students who will most benefit from its programs. The local schools’ curricula, Hassan says, might not provide enough of an academic challenge. “We can’t teach everyone and accommodate everyone because there are a lot of schools,” Hassan says, “but we offer another place for knowledge.” 

      The hackathon helps students develop data science and robotics skills. Before they start coding, students have to convince ADAI Circle teachers that their designs are viable, answering questions like, “What problem are you solving?” and “How will this help the community?” A community-oriented mindset is just as important to the curriculum.

      In addition to the practical skills Hassan gained from Emerging Talent, he leveraged the program’s network to help his community. Thanks to a social media connection Hassan made with the nongovernmental organization Give Internet after one of Emerging Talent’s virtual events, Give Internet brought internet access to ADAI Circle.

      Bridging the AI gap to unmet communities

      In 2023, ADAI Circle connected with another MIT Open Learning program, Responsible AI for Social Empowerment and Education (RAISE), which led to a pilot test of a project-based AI curriculum for middle school students. The Responsible AI for Computational Action (RAICA) curriculum equipped ADAI Circle students with AI skills for chatbots and natural language processing. 

      “I liked that program because it was based on what we’re teaching at the center,” Hassan says, speaking of his organization’s mission of bridging the AI gap to reach unmet communities.

      The RAICA curriculum was designed by education experts at MIT Scheller Teacher Education Program (STEP Lab) and AI experts from MIT Personal Robots group and MIT App Inventor. ADAI Circle teachers gave detailed feedback about the pilot to the RAICA team. During weekly meetings with Glenda Stump, education research scientist for RAICA and J-WEL, and Angela Daniel, teacher development specialist for RAICA, the teachers discussed their experiences, prepared for upcoming lessons, and translated the learning materials in real time. 

      “We are trying to create a curriculum that’s accessible worldwide and to students who typically have little or no access to technology,” says Mary Cate Gustafson-Quiett, curriculum design manager at STEP Lab and project manager for RAICA. “Working with ADAI and students in a refugee camp challenged us to design in more culturally and technologically inclusive ways.”

      Gustafson-Quiett says the curriculum feedback from ADAI Circle helped inform how RAICA delivers teacher development resources to accommodate learning environments with limited internet access. “They also exposed places where our team’s western ideals, specifically around individualism, crept into activities in the lesson and contrasted with their more communal cultural beliefs,” she says.

      Eager to introduce more MIT-developed AI resources, Hassan also shared MIT RAISE’s Day of AI curricula with ADAI Circle teachers. The new ChatGPT module gave students the chance to level up their chatbot programming skills that they gained from the RAICA module. Some of the advanced students are taking initiative to use ChatGPT API to create their own projects in education.

      “We don’t want to tell them what to do, we want them to come up with their own ideas,” Hassan says.

      Although ADAI Circle faces many challenges, Hassan says his team is addressing them one by one. Last year, they didn’t have electricity in their Innovation Hub, but they solved that. This year, they achieved a stable internet connection that’s one of the fastest in Malawi. Next up, they are hoping to secure more devices for their students, create more jobs, and add additional hubs throughout the community. The work is never done, but Hassan is starting to see the impact that ADAI Circle is making. 

      “For those who want to learn data science, let’s let them learn,” Hassan says. More

    • 138 Shares159 Views

      in Data Management & Statistics

      Creating new skills and new connections with MIT’s Quantitative Methods Workshop

      by

      Starting on New Year’s Day, when many people were still clinging to holiday revelry, scores of students and faculty members from about a dozen partner universities instead flipped open their laptops for MIT’s Quantitative Methods Workshop, a jam-packed, weeklong introduction to how computational and mathematical techniques can be applied to neuroscience and biology research. But don’t think of QMW as a “crash course.” Instead the program’s purpose is to help elevate each participant’s scientific outlook, both through the skills and concepts it imparts and the community it creates.

      “It broadens their horizons, it shows them significant applications they’ve never thought of, and introduces them to people whom as researchers they will come to know and perhaps collaborate with one day,” says Susan L. Epstein, a Hunter College computer science professor and education coordinator of MIT’s Center for Brains, Minds, and Machines, which hosts the program with the departments of Biology and Brain and Cognitive Sciences and The Picower Institute for Learning and Memory. “It is a model of interdisciplinary scholarship.”

      This year 83 undergraduates and faculty members from institutions that primarily serve groups underrepresented in STEM fields took part in the QMW, says organizer Mandana Sassanfar, senior lecturer and director of diversity and science outreach across the four hosting MIT entities. Since the workshop launched in 2010, it has engaged more than 1,000 participants, of whom more than 170 have gone on to participate in MIT Summer Research Programs (such as MSRP-BIO), and 39 have come to MIT for graduate school.

      Individual goals, shared experience

      Undergraduates and faculty in various STEM disciplines often come to QMW to gain an understanding of, or expand their expertise in, computational and mathematical data analysis. Computer science- and statistics-minded participants come to learn more about how such techniques can be applied in life sciences fields. In lectures; in hands-on labs where they used the computer programming language Python to process, analyze, and visualize data; and in less formal settings such as tours and lunches with MIT faculty, participants worked and learned together, and informed each other’s perspectives.

      Brain and Cognitive Sciences Professor Nancy Kanwisher delivers a lecture in MIT’s Building 46 on functional brain imaging to QMW participants.

      Photo: Mandana Sassanfar

      Previous item
      Next item

      And regardless of their field of study, participants made connections with each other and with the MIT students and faculty who taught and spoke over the course of the week.

      Hunter College computer science sophomore Vlad Vostrikov says that while he has already worked with machine learning and other programming concepts, he was interested to “branch out” by seeing how they are used to analyze scientific datasets. He also valued the chance to learn the experiences of the graduate students who teach QMW’s hands-on labs.

      “This was a good way to explore computational biology and neuroscience,” Vostrikov says. “I also really enjoy hearing from the people who teach us. It’s interesting to hear where they come from and what they are doing.”

      Jariatu Kargbo, a biology and chemistry sophomore at University of Maryland Baltimore County, says when she first learned of the QMW she wasn’t sure it was for her. It seemed very computation-focused. But her advisor Holly Willoughby encouraged Kargbo to attend to learn about how programming could be useful in future research — currently she is taking part in research on the retina at UMBC. More than that, Kargbo also realized it would be a good opportunity to make connections at MIT in advance of perhaps applying for MSRP this summer.

      “I thought this would be a great way to meet up with faculty and see what the environment is like here because I’ve never been to MIT before,” Kargbo says. “It’s always good to meet other people in your field and grow your network.”

      QMW is not just for students. It’s also for their professors, who said they can gain valuable professional education for their research and teaching.

      Fayuan Wen, an assistant professor of biology at Howard University, is no stranger to computational biology, having performed big data genetic analyses of sickle cell disease (SCD). But she’s mostly worked with the R programming language and QMW’s focus is on Python. As she looks ahead to projects in which she wants analyze genomic data to help predict disease outcomes in SCD and HIV, she says a QMW session delivered by biology graduate student Hannah Jacobs was perfectly on point.

      “This workshop has the skills I want to have,” Wen says.

      Moreover, Wen says she is looking to start a machine-learning class in the Howard biology department and was inspired by some of the teaching materials she encountered at QMW — for example, online curriculum modules developed by Taylor Baum, an MIT graduate student in electrical engineering and computer science and Picower Institute labs, and Paloma Sánchez-Jáuregui, a coordinator who works with Sassanfar.

      Tiziana Ligorio, a Hunter College computer science doctoral lecturer who together with Epstein teaches a deep machine-learning class at the City University of New York campus, felt similarly. Rather than require a bunch of prerequisites that might drive students away from the class, Ligorio was looking to QMW’s intense but introductory curriculum as a resource for designing a more inclusive way of getting students ready for the class.

      Instructive interactions

      Each day runs from 9 a.m. to 5 p.m., including morning and afternoon lectures and hands-on sessions. Class topics ranged from statistical data analysis and machine learning to brain-computer interfaces, brain imaging, signal processing of neural activity data, and cryogenic electron microscopy.

      “This workshop could not happen without dedicated instructors — grad students, postdocs, and faculty — who volunteer to give lectures, design and teach hands-on computer labs, and meet with students during the very first week of January,” Saassanfar says.

      MIT assistant professor of biology Brady Weissbourd (center) converses with QMW student participants during a lunch break.

      Photo: Mandana Sassanfar

      Previous item
      Next item

      The sessions surround student lunches with MIT faculty members. For example, at midday Jan. 2, assistant professor of biology Brady Weissbourd, an investigator in the Picower Institute, sat down with seven students in one of Building 46’s curved sofas to field questions about his neuroscience research in jellyfish and how he uses quantitative techniques as part of that work. He also described what it’s like to be a professor, and other topics that came to the students’ minds.

      Then the participants all crossed Vassar Street to Building 26’s Room 152, where they formed different but similarly sized groups for the hands-on lab “Machine learning applications to studying the brain,” taught by Baum. She guided the class through Python exercises she developed illustrating “supervised” and “unsupervised” forms of machine learning, including how the latter method can be used to discern what a person is seeing based on magnetic readings of brain activity.

      As students worked through the exercises, tablemates helped each other by supplementing Baum’s instruction. Ligorio, Vostrikov, and Kayla Blincow, assistant professor of biology at the University of the Virgin Islands, for instance, all leapt to their feet to help at their tables.

      Hunter College lecturer of computer science Tiziana Ligorio (standing) explains a Python programming concept to students at her table during a workshop session.

      Photo: David Orenstein

      Previous item
      Next item

      At the end of the class, when Baum asked students what they had learned, they offered a litany of new knowledge. Survey data that Sassanfar and Sánchez-Jáuregui use to anonymously track QMW outcomes, revealed many more such attestations of the value of the sessions. With a prompt asking how one might apply what they’ve learned, one respondent wrote: “Pursue a research career or endeavor in which I apply the concepts of computer science and neuroscience together.”

      Enduring connections

      While some new QMW attendees might only be able to speculate about how they’ll apply their new skills and relationships, Luis Miguel de Jesús Astacio could testify to how attending QMW as an undergraduate back in 2014 figured into a career where he is now a faculty member in physics at the University of Puerto Rico Rio Piedras Campus. After QMW, he returned to MIT that summer as a student in the lab of neuroscientist and Picower Professor Susumu Tonegawa. He came back again in 2016 to the lab of physicist and Francis Friedman Professor Mehran Kardar. What’s endured for the decade has been his connection to Sassanfar. So while he was once a student at QMW, this year he was back with a cohort of undergraduates as a faculty member.

      Michael Aldarondo-Jeffries, director of academic advancement programs at the University of Central Florida, seconded the value of the networking that takes place at QMW. He has brought students for a decade, including four this year. What he’s observed is that as students come together in settings like QMW or UCF’s McNair program, which helps to prepare students for graduate school, they become inspired about a potential future as researchers.

      “The thing that stands out is just the community that’s formed,” he says. “For many of the students, it’s the first time that they’re in a group that understands what they’re moving toward. They don’t have to explain why they’re excited to read papers on a Friday night.”

      Or why they are excited to spend a week including New Year’s Day at MIT learning how to apply quantitative methods to life sciences data. More

    • 100 Shares199 Views

      in Data Management & Statistics

      Blueprint Labs launches a charter school research collaborative

      by

      Over the past 30 years, charter schools have emerged as a prominent yet debated public school option. According to the National Center for Education Statistics, 7 percent of U.S. public school students were enrolled in charter schools in 2021, up from 4 percent in 2010. Amid this expansion, families and policymakers want to know more about charter school performance and its systemic impacts. While researchers have evaluated charter schools’ short-term effects on student outcomes, significant knowledge gaps still exist. 

      MIT Blueprint Labs aims to fill those gaps through its Charter School Research Collaborative, an initiative that brings together practitioners, policymakers, researchers, and funders to make research on charter schools more actionable, rigorous, and efficient. The collaborative will create infrastructure to streamline and fund high-quality, policy-relevant charter research. 

      Joshua Angrist, MIT Ford Professor of Economics and a Blueprint Labs co-founder and director, says that Blueprint Labs hopes “to increase [its] impact by working with a larger group of academic and practitioner partners.” A nonpartisan research lab, Blueprint’s mission is to produce the most rigorous evidence possible to inform policy and practice. Angrist notes, “The debate over charter schools is not always fact-driven. Our goal at the lab is to bring convincing evidence into these discussions.”

      Collaborative kickoff

      The collaborative launched with a two-day kickoff in November. Blueprint Labs welcomed researchers, practitioners, funders, and policymakers to MIT to lay the groundwork for the collaborative. Over 80 participants joined the event, including leaders of charter school organizations, researchers at top universities and institutes, and policymakers and advocates from a variety of organizations and education agencies. 

      Through a series of panels, presentations, and conversations, participants including Rhode Island Department of Education Commissioner Angélica Infante-Green, CEO of Noble Schools Constance Jones, former Knowledge is Power Program CEO Richard Barth, president and CEO of National Association of Charter School Authorizers Karega Rausch, and many others discussed critical topics in the charter school space. These conversations influenced the collaborative’s research agenda. 

      Several sessions also highlighted how to ensure that the research process includes diverse voices to generate actionable evidence. Panelists noted that researchers should be aware of the demands placed on practitioners and should carefully consider community contexts. In addition, collaborators should treat each other as equal partners. 

      Parag Pathak, the Class of 1922 Professor of Economics at MIT and a Blueprint Labs co-founder and director, explained the kickoff’s aims. “One of our goals today is to begin to forge connections between [attendees]. We hope that [their] conversations are the launching point for future collaborations,” he stated. Pathak also shared the next steps for the collaborative: “Beginning next year, we’ll start investing in new research using the agenda [developed at this event] as our guide. We will also support new partnerships between researchers and practitioners.”

      Research agenda

      The discussions at the kickoff informed the collaborative’s research agenda. A recent paper summarizing existing lottery-based research on charter school effectiveness by Sarah Cohodes, an associate professor of public policy at the University of Michigan, and Susha Roy, an associate policy researcher at the RAND Corp., also guides the agenda. Their review finds that in randomized evaluations, many charter schools increase students’ academic achievement. However, researchers have not yet studied charter schools’ impacts on long-term, behavioral, or health outcomes in depth, and rigorous, lottery-based research is currently limited to a handful of urban centers. 

      The current research agenda focuses on seven topics:

      the long-term effects of charter schools;
      the effect of charters on non-test score outcomes;
      which charter school practices have the largest effect on performance;
      how charter performance varies across different contexts;
      how charter school effects vary with demographic characteristics and student background;
      how charter schools impact non-student outcomes, like teacher retention; and
      how system-level factors, such as authorizing practices, impact charter school performance.
      As diverse stakeholders’ priorities continue to shift and the collaborative progresses, the research agenda will continue to evolve.

      Information for interested partners

      Opportunities exist for charter leaders, policymakers, researchers, and funders to engage with the collaborative. Stakeholders can apply for funding, help shape the research agenda, and develop new research partnerships. A competitive funding process will open this month.

      Those interested in receiving updates on the collaborative can fill out this form. Please direct questions to chartercollab@mitblueprintlabs.org. More

    • 113 Shares139 Views

      in Data Management & Statistics

      Bridging the gap between preschool policy, practice, and research

      by

      Preschool in the United States has grown dramatically in the past several decades. From 1970 to 2018, preschool enrollment increased from 38 percent to 64 percent of eligible students. Fourteen states are currently discussing preschool expansion, with seven likely to pass some form of universal eligibility within the next calendar year. Amid this expansion, families, policymakers, and practitioners want to better understand preschools’ impacts and the factors driving preschool quality. 

      To address these and other questions, MIT Blueprint Labs recently held a Preschool Research Convening that brought researchers, funders, practitioners, and policymakers to Nashville, Tennessee, to discuss the future of preschool research. Parag Pathak, the Class of 1922 Professor of Economics at MIT and a Blueprint Labs co-founder and director, opened by sharing the goals of the convening: “Our goals for the next two days are to identify pressing, unanswered research questions and connect researchers, practitioners, policymakers, and funders. We also hope to craft a compelling research agenda.”

      Pathak added, “Given preschool expansion nationwide, we believe now is the moment to centralize our efforts and create knowledge to inform pressing decisions. We aim to generate rigorous preschool research that will lead to higher-quality and more equitable preschool.”

      Over 75 participants hailing from universities, early childhood education organizations, school districts, state education departments, and national policy organizations attended the convening, held Nov. 13-14. Through panels, presentations, and conversations, participants discussed essential subjects in the preschool space, built the foundations for valuable partnerships, and formed an actionable and inclusive research agenda.

      Research presented

      Among research works presented was a recent paper by Blueprint Labs affiliate Jesse Bruhn, an assistant professor of economics at Brown University and co-author Emily Emick, also of Brown, reviewing the state of lottery-based preschool research. They found that random evaluations from the past 60 years demonstrate that preschool improves children’s short-run academic outcomes, but those effects fade over time. However, positive impacts re-emerge in the long term through improved outcomes like high school graduation and college enrollment. Limited rigorous research studies children’s behavioral outcomes or the factors that lead to high-quality preschool, though trends from preliminary research suggest that full-day programs, language immersion programs, and specific curricula may benefit children.  

      An earlier Blueprint Labs study that was also presented at the convening is the only recent lottery-based study to provide insight on preschool’s long-term impacts. The work, conducted by Pathak and two others, reveals that enrolling in Boston Public Schools’ universal preschool program boosts children’s likelihood of graduating high school and enrolling in college. Yet, the preschool program had little detectable impact on elementary, middle, and high school state standardized test scores. Students who attended Boston preschool were less likely to be suspended or incarcerated in high school. However, research on preschool’s impacts on behavioral outcomes is limited; it remains an important area for further study. Future work could also fill in other gaps in research, such as access, alternative measures of student success, and variation across geographic contexts and student populations.

      More data sought

      State policy leaders also spoke at the event, including Lisa Roy, executive director of the Colorado Department of Early Childhood, and Sarah Neville-Morgan, deputy superintendent in the Opportunities for All Branch at the California Department of Education. Local practitioners, such as Elsa Holguín, president and CEO of the Denver Preschool Program, and Kristin Spanos, CEO of First 5 Alameda County, as well as national policy leaders including Lauren Hogan, managing director of policy and professional advancement at the National Association for the Education of Young Children, also shared their perspectives. 

      In panel discussions held throughout the kickoff, practitioners, policymakers, and researchers shared their perspectives on pressing questions for future research, including: What practices define high-quality preschool? How does preschool affect family systems and the workforce? How can we expand measures of effectiveness to move beyond traditional assessments? What can we learn from preschool’s differential impacts across time, settings, models, and geographies?

      Panelists also discussed the need for reliable data, sharing that “the absence of data allows the status quo to persist.” Several sessions focused on involving diverse stakeholders in the research process, highlighting the need for transparency, sensitivity to community contexts, and accessible communication about research findings.

      On the second day of the Preschool Research Convening, Pathak shared with attendees, “One of our goals… is to forge connections between all of you in this room and support new partnerships between researchers and practitioners. We hope your conversations are the launching pad for future collaborations.” Jason Sachs, the deputy director of early learning at the Bill and Melinda Gates Foundation and former director of early childhood at Boston Public Schools, provided closing remarks.

      The convening laid the groundwork for a research agenda and new research partnerships that can help answer questions about what works, in what context, for which kids, and under which conditions. Answers to these questions will be fundamental to ensure preschool expands in the most evidence-informed and equitable way possible.

      With this goal in mind, Blueprint Labs aims to create a new Preschool Research Collaborative to equip practitioners, policymakers, funders, and researchers with rigorous, actionable evidence on preschool performance. Pathak states, “We hope this collaborative will foster evidence-based decision-making that improves children’s short- and long-term outcomes.” The connections and research agenda formed at the Preschool Research Convening are the first steps toward achieving that goal. More

    • 63 Shares189 Views

      in Data Management & Statistics

      “MIT can give you ‘superpowers’”

      by

      Speaking at the virtual MITx MicroMasters Program Joint Completion Celebration last summer, Diogo da Silva Branco Magalhães described watching a Spider-Man movie with his 8-year-old son and realizing that his son thought MIT was a fictional entity that existed only in the Marvel universe.

      “I had to tell him that MIT also exists in the real world, and that some of the programs are available online for everyone,” says da Silva Branco Magalhães, who earned his credential in the MicroMasters in Statistics and Data Science program. “You don’t need to be a superhero to participate in an MIT program, but MIT can give you ‘superpowers.’ In my case, the superpower that I was looking to acquire was a better understanding of the key technologies that are shaping the future of transportation.

      Part of MIT Open Learning, the MicroMasters programs have drawn in almost 1.4 million learners, spanning nearly every country in the world. More than 7,500 people have earned their credentials across the MicroMasters programs, including: Statistics and Data Science; Supply Chain Management; Data, Economics, and Design of Policy; Principles of Manufacturing; and Finance. 

      Earning his MicroMasters credential not only gave da Silva Branco Magalhães a strong foundation to tackle more complex transportation problems, but it also opened the door to pursuing an accelerated graduate degree via a Northwestern University online program.

      Learners who earn their MicroMasters credentials gain the opportunity to apply to and continue their studies at a pathway school. The MicroMasters in Statistics and Data Science credential can be applied as credit for a master’s program at more than 30 universities, as well as MIT’s PhD Program in Social and Engineering Systems. Da Silva Branco Magalhães, originally from Portugal and now based in Australia, seized this opportunity and enrolled in Northwestern University’s Master’s in Data Science for MIT MicroMasters Credential Holders. 

      The pathway to an enhanced career

      The pathway model launched in 2016 with the MicroMasters in Supply Chain Management. Now, there are over 50 pathway institutions that offer more than 100 different programs for master’s degrees. With pathway institutions located around the world, MicroMasters credential holders can obtain master’s degrees from local residential or virtual programs, at a location convenient to them. They can receive credit for their MicroMasters courses upon acceptance, providing flexibility for online programs and also shortening the time needed on site for residential programs.

      “The pathways expand opportunities for learners, and also help universities attract a broader range of potential students, which can enrich their programs,” says Dana Doyle, senior director for the MicroMasters Program at MIT Open Learning. “This is a tangible way we can achieve our mission of expanding education access.”

      Da Silva Branco Magalhães began the MicroMasters in Statistics and Data Science program in 2020, ultimately completing the program in 2022.

      “After having worked for 20 years in the transportation sector in various roles, I realized I was no longer equipped as a professional to deal with the new technologies that were set to disrupt the mobility sector,” says da Silva Branco Magalhães. “It became clear to me that data and AI were the driving forces behind new products and services such as autonomous vehicles, on-demand transport, or mobility as a service, but I didn’t really understand how data was being used to achieve these outcomes, so I needed to improve my knowledge.”

      July 2023 MicroMasters Program Joint Completion Celebration for SCM, DEDP, PoM, SDS, and FinVideo: MIT Open Learning

      The MicroMasters in Statistics and Data Science was developed by the MIT Institute for Data, Systems, and Society and MITx. Credential holders are required to complete four courses equivalent to graduate-level courses in statistics and data science at MIT and a capstone exam comprising four two-hour proctored exams.

      “The content is world-class,” da Silva Branco Magalhães says of the program. “Even the most complex concepts were explained in a very intuitive way. The exercises and the capstone exam are challenging and stimulating — and MIT-level — which makes this credential highly valuable in the market.”

      Da Silva Branco Magalhães also found the discussion forum very useful, and valued conversations with his colleagues, noting that many of these discussions later continued after completion of the program.

      Gaining analysis and leadership skills

      Now in the Northwestern pathway program, da Silva Branco Magalhães finds that the MicroMasters in Statistics and Data Science program prepared him well for this next step in his studies. The nine-course, accelerated, online master’s program is designed to offer the same depth and rigor of Northwestern’s 12-course MS in Data Science program, aiming to help students build essential analysis and leadership skills that can be directly implemented into the professional realm. Students learn how to make reliable predictions using traditional statistics and machine learning methods.

      Da Silva Branco Magalhães says he has appreciated the remote nature of the Northwestern program, as he started it in France and then completed the first three courses in Australia. He also values the high number of elective courses, allowing students to design the master’s program according to personal preferences and interests.

      “I want to be prepared to meet the challenges and seize the opportunities that AI and data science technologies will bring to the professional realm,” he says. “With this credential, there are no limits to what you can achieve in the field of data science.” More

    • 50 Shares189 Views

      in Data Management & Statistics

      Learning how to learn

      by

      Suppose you need to be on today’s only ferry to Martha’s Vineyard, which leaves at 2 p.m. It takes about 30 minutes (on average) to drive from where you are to the terminal. What time should you leave?

      This is one of many common real-life examples used by Richard “Dick” Larson, a post-tenure professor in the MIT Institute for Data, Systems, and Society (IDSS), to explore exemplary problem-solving in his new book “Model Thinking for Everyday Life: How to Make Smarter Decisions.”

      Larson’s book synthesizes a lifelong career as an MIT professor and researcher, highlighting crucial skills underpinning all empirical, rational, and critical thinking. “Critical thinkers are energetic detectives … always seeking the facts,” he says. “Additional facts may surface that can result in modified conclusions … A critical thinker is aware of the pitfalls of human intuition.”

      For Larson, “model” thinking means not only thinking aided by conceptual and/or mathematical models, but a broader mode of critical thought that is informed by STEM concepts and worthy of emulation.

      In the ferry example, a key concept at play is uncertainty. Accounting for uncertainty is a core challenge faced by systems engineers, operations researchers, and modelers of complex networks — all hats Larson has worn in over half a century at MIT. 

      Uncertainty complicates all prediction and decision-making, and while statistics offers tactics for managing uncertainty, “Model Thinking” is not a math textbook. There are equations for the math-curious, but it doesn’t take a degree from MIT to understand that

      an average of 30 minutes would cover a range of times, some shorter, some longer;
      outliers can exist in the data, like the time construction traffic added an additional 30 minutes
      “about 30 minutes” is a prediction based on past experience, not current information (road closures, accidents, etc.); and
      the consequence for missing the ferry is not a delay of hours, but a full day — which might completely disrupt the trip or its purpose.
      And so, without doing much explicit math, you calculate variables, weigh the likelihood of different outcomes against the consequences of failure, and choose a departure time. Larson’s conclusion is one championed by dads everywhere: Leave on the earlier side, just in case. 

      “The world’s most important, invisible profession”

      Throughout Larson’s career at MIT, he has focused on the science of solving problems and making better decisions. “Faced with a new problem, people often lack the ability to frame and formulate it using basic principles,” argues Larson. “Our emphasis is on problem framing and formulation, with mathematics and physics playing supporting roles.”

      This is operations research, which Larson calls “the world’s most important invisible profession.” Formalized as a field during World War II, operations researchers use data and models to try to derive the “physics” of complex systems. The goal is typically optimizing things like scheduling, routing, simulation, prediction, planning, logistics, and queueing, for which Larson is especially well-known. A frequent media expert on the subject, he earned the moniker “Dr. Q” — and his research has led to new approaches for easing congestion in urban traffic, fast-food lines, and banks.

      Larson’s experience with complex systems provides a wealth of examples to draw on, but he is keen to demonstrate that his purview includes everyday decisions, and that “Model Thinking” is a book for everyone. 

      “Everybody uses models, whether they realize it or not,” he says. “If you have a bunch of errands to do, and you try to plan out the order to do them so you don’t have to drive as much, that’s more or less the ‘traveling salesman’ problem, a classic from operations research. Or when someone is shopping for groceries and thinking about how much of each product they need — they’re basically using an inventory management model of their pantry.”

      Larson’s takeaway is that since we all use conceptual models for thinking, planning, and decision-making, then understanding how our minds use models, and learning to use them more intentionally, can lead to clearer thinking, better planning, and smarter decision-making — especially when they are grounded in principles drawn from math and physics.

      Passion for the process

      Teaching STEM principles has long been a mission of Larson’s, who co-founded MIT BLOSSOMS (Blended Learning Open Source Science or Math Studies) with his late wife, Mary Elizabeth Murray. BLOSSOMS provides free, interactive STEM lessons and videos for primary school students around the world. Some of the exercises in “Model Thinking” refer to these videos as well.

      “A child’s educational opportunities shouldn’t be limited by where they were born or the wealth of their parents,” says Larson of the enterprise. 

      It was also Murray who encouraged Larson to write “Model Thinking.” “She saw how excited I was about it,” he says. “I had the choice of writing a textbook on queuing, say, or something else. It didn’t excite me at all.”

      Larson’s passion is for the process, not the answer. Throughout the book, he marks off opportunities for active learning with an icon showing the two tools necessary to complete each task: a sharpened pencil and a blank sheet of paper. 

      “Many of us in the age of instant Google searches have lost the ability — or perhaps the patience — to undertake multistep problems,” he argues.

      Model thinkers, on the other hand, understand and remember solutions better for having thought through the steps, and can better apply what they’ve learned to future problems. Larson’s “homework” is to do critical thinking, not just read about it. By working through thought experiments and scenarios, readers can achieve a deeper understanding of concepts like selection bias, random incidence, and orders of magnitude, all of which can present counterintuitive examples to the uninitiated.

      For Larson, who jokes that he is “an evangelist for models,” there is no better way to learn than by doing — except perhaps to teach. “Teaching a difficult topic is our best way to learn it ourselves, is an unselfish act, and bonds the teacher and learner,” he writes.

      In his long career as an educator and education advocate, Larson says he has always remained a learner himself. His love for learning illuminates every page of “Model Thinking,” which he hopes will provide others with the enjoyment and satisfaction that comes from learning new things and solving complex problems.

      “You will learn how to learn,” Larson says. “And you will enjoy it!” More

    • 175 Shares189 Views

      in Data Management & Statistics

      3 Questions: A new PhD program from the Center for Computational Science and Engineering

      by

      This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

      Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

      The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

      Q: What is computational science and engineering?

      Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

      Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

      Q: What was the motivation behind creating a standalone PhD program?

      Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

      Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

      Q: How will this impact computing education and research at MIT? 

      Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

      Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique. More