Physics Is a Contact Sport

Several MIT students peering into a spherical apparatus with various wires attached.

Students perform an experiment in relativistic dynamics in MIT’s Junior Lab.
(Image by OCW)

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

If you’re exceptionally gifted, you might be able to learn the established facts of physics by reading books and articles and by attending lectures. But if you want to contribute actively to the field, you need two other forms of expertise: skill in designing and conducting experiments, and a working knowledge of how to communicate your work to other physicists and to the world in general. MIT’s Junior Lab helps students develop firsthand expertise in both these areas.

What Is Junior Lab?

Junior Lab is a sequence of two undergraduate courses, officially designated as 8.13 Experimental Physics I and 8.14 Experimental Physics II, that most physics majors take in the fall and spring of their junior year (hence the name). As Nergis Mavalvala, Associate Head of MIT’s Physics department, explains:

Junior Lab is a keystone course of the MIT physics curriculum. This challenging and memorable course exposes students to diverse techniques in experimental physics, and develops scientific writing and oral presentation skills….Students learn to make measurements using sophisticated apparatus, analyze their data, compare their results to other empirical determinations of the same physical quantities or phenomena, write up their findings as a professional publishable paper, and communicate their results in an oral presentation — all skills with which a practicing physicist must be conversant.

Doing Hands-On Physics

During their year in Junior Lab, students perform a total of ten experiments covering a range of phenomena whose discoveries led to major advances in physics, such as Compton scattering, relativistic dynamics, cosmic-ray muons, radio astrophysics, laser spectroscopy, superconductivity, and quantum information processing. Students work in pairs to set up each experiment, to make measurements, and to analyze and interpret their data. After each experiment, each pair of lab partners participates in a one-hour oral examination and discussion with their instructors. Both students bring their lab notebooks to the oral exam session, and all oral exams are video-recorded so that students can review and refine their presentation technique.

At the end of the fall term, each student delivers a public oral presentation to peers, friends, and faculty in the style of a session at a professional conference. Near the end of the spring term, each pair of lab partners designs and conducts an original, open-ended experiment, after which they summarize their results in a scientific poster presented in an open poster session.

A Wealth of Information

The richness of the Junior Lab experience is reflected in the richness of the materials pertaining to the course on OpenCourseWare. In addition to the syllabus, the course on OCW includes the following:

  • Detailed descriptions of the standard experiments students in Junior Lab perform.
  • A set of guidelines for safety in the lab, including policies to maintain chemical hygiene, environmental safety, electrical safety, radiation safety, cryogenic safety, laser safety, and biological safety.
  • Itemized instructions on how to keep and use a lab notebook to record experimental procedures and results.

For educators and those interested in pedagogical theory, though, the most exciting aspect of Junior Lab on OCW is the wealth of interview videos, in which the course’s professors, other members of the instructional team, and several students share their insights into what’s special about the way Junior Lab is taught. A few highlights:

Junior Lab is based on the notion that the best way to learn physics is experientially, through hands-on learning. Professor Janet Conrad strongly feels that physics is “a contact sport.” In the video clip below, Professor Conrad gives a simple hands-on demonstration of electromagnetic induction that could be used to make physics real even for early elementary students:

(What’s going on in this video? Ordinarily, a dropped object falls half a meter in about a third of a second, but when Professor Conrad drops the magnet into the copper pipe, it takes almost four seconds to fall that far, because the magnet’s motion induces an electric current in the pipe, which in turn generates a magnetic field that brakes the magnet’s fall.)

The structure of the course is also designed to develop skills in collaboration and teamwork in scientific research. Students in Junior Lab don’t just conduct their experiments in teams of two; lab partners also participate in oral exams together, and work together to design their final experiment and to produce and present their poster for the presentation at the end of the spring term. This collaborative approach has clear benefits, but also brings with it some extra challenges, as Professor Gunther Roland explains.

Dr. Sean Robinson, Head of Junior Lab Technical Staff, discusses how the approach to teaching the course has changed in recent years, flipping the classroom to “get the students the information they need at the time when they’re most ready to learn it.” Data analysis, Dr. Robinson says, is best learned as you go along rather than by front-loading information in a lecture hall. Student Henry Shackleton agrees, emphasizing that the independent learning fostered by a flipped-classroom format meshes well with the nature of lab work, in which students are on their own much of the time.

One of the core tenets of Junior Lab is that science communication is a crucial professional competency for anyone wishing to pursue a research career. After all, progress in physics or any other scientific field requires not only that research be conducted and discoveries made, but also that experimental results and discoveries be communicated to other scientists. To help develop students’ communication skills, the instructional team for Junior Lab includes not only scientists but also a communication instructor, Senior Lecturer Atissa Banuazizi. “I think it’s somewhat of a misconception that communication can be separated from the work that scientists do,” Ms. Banuazizi says. “Because so much of what scientists do in their daily lives is communication. If you are a scientist, and you are doing really, really exciting work, that work is not going to have any kind of impact if you can’t tell people about it.”

Whether you’re a student, an independent learner, or an instructor pondering how best to teach the concepts of physics and the skills needed by working scientists, we encourage you to check out the rich collection of Junior Lab course material available to you on OCW.

The OCW course presents all the materials students use to carry out [their assigned] tasks, complemented by instructor, teaching assistant, and student perspectives on how the course is taught. It should serve as a unique guide for students and instructors on how to build and execute experiments, analyze data, and present results in effective written and oral reports.  -Nergis Mavalvala

Doctoral Students Aren’t Lone Wolves: An interview with Brian Charles Williams

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

The Curiosity rover standing on the surface of Mars

The Curiosity Mars rover, a complex, collaboratively-built system based on cognitive robotics. (Image credit: NASA/JPL-Caltech/MSSS)

Robotics and artificial intelligence are fast-paced fields in which researchers constantly have to adapt to new technological developments. But in such fields, progress isn’t always achieved by competitive, individual effort; in many circumstances, cooperation and collaboration are more fruitful approaches. In the interview excerpt below, Brian Charles Williams, a professor at MIT’s Computer Science & Artificial Intelligence Laboratory, describes how he develops learning communities in the graduate-level course 16.412 Cognitive Robotics:

OCW: How is learning different in a course focused on an emerging field like cognitive robotics?

Brian Williams: Students are accustomed to reading chapters in textbooks—material that took decades for scientists to understand. But cognitive robotics is an active research area. It’s moving so quickly that every three years or so it reinvents itself. This course is focused on helping students close the gaps in the research. To be at the cutting edge of research, students need to read across papers and understand core ideas that are developed from a collection of publications. And then they need to be able to reduce that understanding to practice.

There’s also no better way to understand something than to teach it, implement it, and put it in a bigger context of some real-world application. That’s why we have a grand challenge at the center of the course experience.

OCW: Tell us more about the grand challenge.

Brian Williams: I like the idea of learning communities, of everybody trying to learn about a topic together. The grand challenge is a communal learning experience driven by a cutting-edge research question in cognitive robotics that allows us to focus on core reasoning algorithms. Students work in teams to present advanced lectures about different aspects of the topic.

OCW: Why teams?

Brian Williams: It’s important for students to work in teams because research is a collaborative endeavor. The notion that doctoral students are lone wolves is just not accurate. The more students can practice effective collaboration, the better.

It’s also the case that developing lectures is hard work. Just producing a first draft of a lecture can take 20 to 30 hours. And then you need to spend another 6 hours improving it. So, to develop a high-quality lecture, you really need two people working together.

Robot standing in a room.

Domo Robot, developed by Aron Edsinger and Jeff Weber, is able to adapt to novel situations. It is on display in the MIT Computer Science and Artificial Intelligence Lab. (Image by MIT OpenCourseWare.)

OCW: How do you assess student work completed collaboratively?

Brian Williams: That is an interesting problem, because when the whole class does a project collaboratively teams can become too large. When that happens, people begin to feel disenfranchised. What I do to combat that is to make clear from the beginning what elements or materials individuals are responsible for contributing to the project. I have students write down what they are contributing so that I can assess their work accurately.

Another piece of the assessment puzzle is providing good feedback. The place where feedback matters the most is during the dry run for the students’ advanced lectures. A week before the students give their lecture to the class, they do a dry run for the teaching team and receive feedback. The process takes about two and a half hours. We teach them how to capture students’ interest at the beginning of the lecture and how to clarify the main points they want students to learn. We also help them convey the synergies between the main points and encourage them to consider the role of examples in their presentations.

OCW: Are there other components of the grand challenge, in addition to the advanced lectures?

Brian Williams: Yes. As I mentioned, the field of cognitive robotics is moving really fast. What normally happens is that members of the research community will generate tutorials on emerging themes. These tutorials encapsulate core ideas that everybody should know. The problem is that there’s just so much we need to know—but not enough time to write all the tutorials. So some of the students in the class are assigned to write tutorials related to the topic of the grand challenge. And a few others will write corresponding Jupiter or Python notebook problem sets. Along with the lectures, students end up producing materials that are enormously helpful to researchers in the field. This is important because I want them to learn that as scientists, their role is to consolidate ideas and to teach the community.

Man sitting at desk. Bookshelves with books to his left.

Aeronautics and Astronautics Professor Brian Williams in his office on the MIT campus. (Image by MIT OpenCourseWare.)

OCW: It’s interesting that you have the goal of figuring out cognitive robotics as a field, but also how to teach it to others.

Brian Williams: And how to catalyze community. An engaged, collaborative community is absolutely key.

***

You can read more of Professor Williams’s thoughts about teaching 16.412 on the Instructor Insights page of this course.

Keep learning! The following courses and Instructor Insights may be of interest to you:

Another OCW Course Offered by Professor Williams

Image combining data taken by an autonomous vehicle with the views from its windows.16.410 Principles of Autonomy and Decision Making

This course surveys a variety of reasoning, optimization, and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.

Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

More about Robotics and Artificial Intelligence

Wheeled robot carrying doll in its arm.2.12 Introduction to Robotics

This course provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. Weekly laboratories provide experience with servo drives, real-time control, and embedded software. Students design and fabricate working robotic systems in a group-based term project.

Graphic of three figures in an evolutionary arc, starting with a figure standing upright on the left, ending with a person hunched over at a computer on the right.6.034 Artificial Intelligence

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

More on Learning Communities

Illustration of a brain with colors indicating regions involved in social processes, plus three example faces used in social testing and a photo of a large group sitting in a circle.9.70 Social Psychology

In the rather idiosyncratic syllabus for this course, which goes into much more philosophical depth than such documents usually attain, Professor Stephan L. Chorover lays out the principles of the collaborative learning system that formed the basis of his approach to teaching.

A woman on the monitor of a video camera, facing the viewer20.219 Becoming the Next Bill Nye: Writing and Hosting the Educational Show

Elizabeth Choe and Jaime Goldstein discuss the importance of cultivating a sense of community in the classroom, and explain how situating themselves as facilitators-of-learning, rather than omniscient givers-of-knowledge, communicated to students their respect for them as learners.

The faces of the four members of the Beatles in a 4 by 4 grid.21M.299 The Beatles

The Beatles lived an insulated life in the 1960s. They couldn’t go out without being mobbed. As a result, the four of them spent much of their time together, listening to and playing music. In that process, they were constantly learning from each other. Lecturer Teresa Neff discusses the centrality of group learning in her Instructor Insights for this course.

Find insights like these on many other teaching approaches at our Educator Portal.

College search support from your friends at OCW

Photo of three students sitting on a bench, in conversation.

Photo by Jake Belcher.

To all students who are now deep into the autumn ritual of college applications, along with all the other demands of your year: we feel you!

While we can’t join you on college tours or write those application essays, OCW can hopefully support you in a few other ways during this exciting hectic time, as OCW is always free and open for you anytime and anywhere you need it.

Screenshot of the OCW course homepage for 6.0001 Introduction to Computer Science and Programming in Python.Many incoming students use OCW to preview what college studies are like. For instance, 6.0001 Introduction to Computer Science and Programming in Python is one of the most popular courses at MIT and also on OCW. Freely browse through the teaching materials used in every MIT department and major, and go well beyond the short descriptions in most course catalogs: check out OCW lecture notes, readings, assignments and more from introductory core classes as well as advanced electives.

Screenshot of OCW Find by Topic browser.

Our Find Courses by Topic and Find Courses by Department pages make it easy to explore OCW’s collection of over 2500 courses and supplemental resources from 36 MIT departments and programs.

 

Photo of a group of students celebrating in a lab, with one student being held up in the air by the others.The OCW Highlights for High School has collected resources of particular interest to high school students, and their teachers and parents. Check out our exam prep material, lists of introductory OCW courses to guide and inspire your college search, and cool stuff like the ChemLab Boot Camp reality video series.

Screenshot of First Year STEM courses webpage, highlighting Biology courses.If you’re looking to get ahead in your studies of STEM subjects like Math, Physics, and Computer Science check out the First Year STEM Classes from MIT collection, which includes both MITx on edX and OCW courses. Learn from the same material used by first year MIT students to advance your knowledge, and help you prepare for incoming student placement tests.

Screenshot of webpage "Best of the Blogs."Finally, while it’s not actually part of OCW, we’re big fans of the MIT Admissions student blogs for their direct, honest, diverse and personal account of college life. Whether or not you’re applying to MIT, they’re well worth a read.

 

We wish you all the best in your quest for a great college match!

How Would You Like Your Grade? An Interview with George Verghese

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

black lines, indicating elements of heart beats, on red and white graph paper.

Electrocardiogram data, an example of measured signals. (Image courtesy of kenfagerdotcom on flickr. License: CC BY-NC-SA.)

The syllabus for a typical MIT course spells out a familiar grading scheme that assigns fixed percentage weights to the different elements of the course: so many points for attendance and participation, so many for the quizzes or written assignments, and so many for the final exam or final project. Such a system is straightforward to implement and easy for students to understand, but there are times when both students and instructors want a little more flexibility. After all, not all students are the same, and they don’t all have the same needs.

In Spring 2018, Professors George Verghese, Alan V. Oppenheim, and Peter Hagelstein co-taught 6.011 Signals, Systems and Inference, an undergraduate course that covers a broad range of topics pertaining to communication, control, and signal processing. The material is complex, and the instructors support student learning in unique ways. We approached Professor Verghese for his insights on the course’s unusual grading system, and also on how the teaching team uses tutorials and an informal collaborative learning space called the Common Room to help MIT students succeed.

OCW: You offer three grading schemes in the course: regular, lower-friction, and project. This is so interesting! Tell us about your decision to offer students this kind of choice.

George Verghese: Ideally we’d like all students to attend all lectures and recitations, and for most students this is essential to their learning the material well and succeeding in the class. However, student lives can be complicated, their backgrounds and motivations are varied, and they optimize their trajectories through MIT in different ways. I know there is always a handful of students who can master the material with much less interaction, and I am fine with allowing them to do that, without getting in their way—hence the “lower-friction option,” in which the only components of the grade are their scores on the homework, the two quizzes during the term, and on the final exam. Students who opt for this have access to all the material in the class, but not to the tutorial sessions, as we don’t want them using the teaching assistants to help them make up for lecture and recitation material they may have missed. Unfortunately, there are always a couple of students who opt for the lower-friction version who really should not have, and their grade ends up suffering for it. But there are others—and these are the ones for whom this option is intended—who end up near, or even at, the top of the class. More power to them! My only regret is that we lose the benefit of whatever they may have contributed to class discussions if they had attended lectures and recitations.

Those students who do not elect the lower-friction option are, in effect, signing on to attending most lectures and recitations, and 15% of their course grade is allotted to attendance. I don’t actually take attendance directly, but every few lectures I will have them pair up in class to work out some problem related to the lecture material, then turn in their answer sheets at the end of lecture (with their neighbor’s name on their sheet, so they know I’m not looking to grade them on their answers!). Any student who misses a couple of these gets a note from me to urge better attendance. And recitation instructors have a good sense of who is attending and who isn’t, even if they don’t take attendance formally.

Lecture 22 image

An example of binary hypothesis testing from Lecture 22 (PDF).

There are also a few students each semester who feel they’d do better if they had a project to anchor their learning, and also to spread the course grade over (10% of the course grade is assigned to the project for students who choose this option, and the contributions of quizzes and the final exam are correspondingly reduced). Since there are typically only a few such students, I work quite closely with them over the semester, meeting at least every couple of weeks, to ensure the projects are related to course material and are moving along well. Some of these projects turn out to be good demonstrations for lectures in succeeding terms.

OCW: Please describe the tutorials offered to students and tell us about their role in the course.

George Verghese: Our tutorials are run by the teaching assistants on an optional, sign-up basis, limited to 5 students per session. Some students—perhaps a third of the class—attend them very regularly each week, others occasionally or not at all. The idea here is to actively engage the students, have them go the board to work things out, rather than having the teaching assistant give a summary of lecture at the board and then work out problems for the students. The teaching assistants go prepared with a small set of basic problems, simpler than those on homework, and illustrating points that have come up in lecture. However, the tutorials are also teaching assistant office hours, and students are encouraged to come with questions they may have. Any general guidance that the lecturer or the recitation instructors may have for the teaching assistants usually comes at our weekly staff meeting, held on Monday to set plans and directions for the week and beyond, but we typically leave the teaching assistants to come up with specific problems for their tutorials, perhaps in coordination with each other. The teaching assistants also take turns helping the lecturer generate the problem sets and solutions.

OCW: Please tell us about the role of the Common Room in the course. What was the impact of having a space where students could informally ask questions and work alongside each other on course assignments?

George Verghese: I think the evening Common Room is one of the best elements of the class, for those students—around a third to half of the class—who take advantage of it. I got the idea for it many years ago when visiting another university campus center after dinner, and found clusters of students sitting at desks and working collaboratively on homework and projects, though with no instructors in sight. For 6.011 Signals, Systems and Inference, we reserve a classroom for the three or four evenings that precede the day homework is due, and guarantee that at least one of the staff will be present there for 1.5-2 hours; usually we have the lecturer or a recitation instructor, as well as a teaching assistant.

“Our staff invariably finds the Common Room to be the most rewarding of the various settings in which they interact with students.”—GEORGE VERGHESE

We find students working individually as well as collaboratively, and periodically interacting with the staff, either at the board or at their desk—very immersed and engaged in the homework problems, and in sorting out ideas and misconceptions related to these. The staff will typically respond to student questions with other (well chosen!) questions or hints that guide them along, rather than with answers—and that makes for a very fruitful dynamic. We have never found the Common Room misused as a place to come and get fellow students to feed one solutions to the homework. I would absolutely recommend this to other faculty, if they have the staff resources and time. Our staff invariably finds the Common Room to be the most rewarding of the various settings in which they interact with students, and it is where they get to know their students best.

***

You can read more of Professor Verghese’s thoughts about teaching 6.011 on the Instructor Insights page of this course.

Keep learning! The following courses and Instructor Insights may be of interest to you:

More OCW Courses Offered by Professors Verghese and Oppenheim

Artist's depiction of the Cassini spacecraft, with Saturn in the foreground and a dark blue, starry background.Introduction to EECS II: Digital Communication Systems

An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course—bits, signals, and packets—cover three corresponding layers of abstraction that form the basis of communication systems like the Internet.

The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.

An audio compact disc.Discrete-Time Signal Processing

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications.

More about Communication, Control, and Signal Processing

6-003f11-th.jpgSignals and Systems

This course, a prerequisite for course 6.011, covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.

2-14s14-thAnalysis and Design of Feedback Control Systems

This course develops the fundamentals of feedback control using linear transfer function system models. Topics covered include analysis in time and frequency domains; design in the s-plane (root locus) and in the frequency domain (loop shaping); describing functions for stability of certain non-linear systems; extension to state variable systems and multivariable control with observers; discrete and digital hybrid systems and use of z-plane design. Students will complete an extended design case study.

2-161f08-thSignal Processing: Continuous and Discrete

This course provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Topics covered include spectral analysis, filter design, system identification, and simulation in continuous and discrete-time domains. The emphasis is on practical problems with laboratory exercises.

More on Assessment and Grading

6-01scs11-thIntroduction to Electrical Engineering and Computer Science I

Professor Dennis Freeman reflects on the advantages and limitations of using oral exams to assess student learning, and considers how online exams might help instructors offer scalable assessments that are personalized and productive.

6-034f10-thArtificial Intelligence

Teaching assistants Jessica Noss and Dylan Holmes describe the unusual grading system for this course, in which the final exam is optional, with each of its sections serving as a make-up exam for one of the course’s regular quizzes.

18-821s13-thProject Laboratory in Mathematics

Project Laboratory in Mathematics is designed to give students a sense of what it’s like to do mathematical research. In the Grading section of this course, Professor Haynes Miller and Susan Ruff describe their approach to grading and their experiences in developing (and revising!) grading rubrics.

Find insights like these on many other teaching approaches at our Educator Portal.

OCW’s Greatest Hits: Architecture and Urban Studies and Planning

It’s time for a new post in our Greatest Hits series, highlighting individual MIT departments through a handpicked selection from their most-visited OCW courses. This month we feature the departments of Architecture and Urban Studies and Planning.

Photo of interlocking wooden forms.

This model from a student’s final project in 4.111 Introduction to Architecture & Environmental Design demonstrates the relationship between object and void. (Courtesy of Johanna Greenspan-Johnston. Used with permission.)

Architecture

  • 4.111 Introduction to Architecture & Environmental Design, taught by Lorena Bello Gomez
    This course provides a foundation to the design of the environment from the scale of the object, to the building to the larger territory. The design disciplines of architecture as well as urbanism and landscape are examined in context of the larger influence of the arts and sciences.
  • 4.125 Architecture Studio: Building in Landscapes, taught by Professor Jan Wampler
    This undergraduate design studio “introduces skills needed to build within a landscape establishing continuities between the built and natural world. Students learn to build appropriately through analysis of landscape and climate for a chosen site, and to conceptualize design decisions through drawings and models.”
  • 4.241J Theory of City Form, taught by Professor Julian Beinart
    This course covers theories about the form that settlements should take and attempts a distinction between descriptive and normative theory by examining examples of various theories of city form over time. Case studies will highlight the origins of the modern city and theories about its emerging form, including the transformation of the nineteenth-century city and its organization.
  • 4.341 Introduction to Photography and Related Media, taught by Andrea Frank et al
    This course provides practical instruction in the fundamentals of analog and digital SLR and medium/large format camera operation, film exposure and development, black and white darkroom techniques, digital imaging, and studio lighting.”
  • 4.401 Introduction to Building Technology, taught by Professor Marilyne Andersen
    This course provides a fundamental understanding of the physics related to buildings and an overview of the various issues that have to be adequately combined to offer the occupants a physical, functional and psychological well-being. Students are guided through the different components, constraints and systems of a work of architecture. These are examined both independently and in the manner in which they interact and affect one another.

Photo of feet along a brick-paved path.

The Post Office Square in Boston served as the site of a student’s project in 11.309J Sensing Place: Photography as Inquiry. (Image courtesy of Francisca Rojas. Used with permission.)

Urban Studies and Planning

  • 11.001J Introduction to Urban Design and Development, taught by Professor Susan Silberberg
    This course examines the evolving structure of cities and the way that cities, suburbs, and metropolitan areas can be designed and developed. Boston and other American cities are studied to see how physical, social, political and economic forces interact to shape and reshape cities over time.
  • 11.011 The Art and Science of Negotiation, taught by David Laws
    This course provides an introduction to bargaining and negotiation in public, business, and legal settings. It combines a “hands-on” skill-building orientation with a look at pertinent social theory. Strategy, communications, ethics, and institutional influences are examined as they influence the ability of actors to analyze problems, negotiate agreements, and resolve disputes in social, organizational, and political circumstances characterized by interdependent interests.
  • 11.126J Economics of Education, taught by Professor Frank Levy
    This class discusses the economic aspects of current issues in education, using both economic theory and econometric and institutional readings. Topics include discussion of basic human capital theory, the growing impact of education on earnings and earnings inequality, statistical issues in determining the true rate of return to education, the labor market for teachers, implications of the impact of computers on the demand for worker skills, the effectiveness of mid-career training for adult workers, the roles of school choice, charter schools, state standards and educational technology in improving K-12 education, and the issue of college financial aid.
  • 11.309J Sensing Place: Photography as Inquiry, taught by Professor Anne Whiston Spirn
    This course explores photography as a disciplined way of seeing or investigating urban landscapes, and expressing ideas. Readings, observations, and photographs form the basis of discussions on light, detail, place, poetics, narrative, and how photography can inform design and planning.
  • 11.431J Real Estate Finance and Investment, taught by Professors David Geltner and Tod McGrath
    This course is an introduction to the most fundamental concepts, principles, analytical methods and tools useful for making investment and finance decisions regarding commercial real estate assets. As the first of a two-course sequence, this course will focus on the basic building blocks and the “micro” level, which pertains to individual properties and deals.

5 tips for getting to know your students

By Sarah Hansen, OCW Educator Project Manager

Several stacked pizza boxes

Professor Catherine Drennan uses pizza forums to connect with students in her large lecture class.

Students learn better when you see them as individuals and care about their success. But it can be challenging to get to know your students when you teach large lecture classes, or interact with a new group of students (or several!) every 15 weeks.  MIT faculty members face these challenges, too. We’ve mined their Instructor Insights to bring you 5 creative ways to get to know your students this semester.

  1. Start Your Lecture Sitting Down

Four yellow dots and the word Life on blue backgroundWith 300-400 students taking Introductory Biology each year, Professor Hazel Sive has ample experience getting to know students in the context of large classes. One of her strategies is to make use of the time before class starts to connect with students. In her Instructor Insights, she notes that “before class, I sometimes walk around the room and meet groups of students. Sometimes I start the lecture sitting down with a group of students and have them introduce themselves to the class at the beginning of the lecture, so that we have a bit of personal interaction going on.”

Still, she admits it can be difficult to get to know every student. “I worry,” she notes, “that if I know the names of some students, and I speak to them by name in class, other students might feel a bit excluded. I don’t like there to be a feeling of, ‘Oh, she didn’t even bother with me.’ … So I always try to make sure that when we’re speaking about our class, we talk about ourselves as a group and that the group is our measure of who we are. I want students to feel that there’s a greater whole, that we’re a community.”

  1. Ask Students to Place Themselves on the Talkativeness Spectrum

Several women breastfeeding babiesGender, Power, Leadership, and the Workplace is an undergraduate discussion-based course that equips students with an analytic framework to understand the roles that gender, race, and class play in defining and determining access to leadership and power in the U.S., especially in the context of the workplace. To get a feel for how to facilitate dialogue with the group of students who took the course in Spring 2014, the instructor, Dr. Mindy Fried, asked students “how they viewed themselves along a spectrum . . . of ‘talkativeness’ . . . (e.g., very talkative to very quiet).” In her Instructor Insights, she notes that “I also asked them what helped them to be more talkative in class. This information provided me with a baseline of understanding about how they viewed themselves.”

Fried goes on to say that, “I didn’t adjust my expectations based on this information. Instead, I provided opportunities for everyone to speak and be heard. I employed various methods to create a ‘safe’ environment where people of all backgrounds and with all opinions could articulate their thoughts and beliefs.”

  1. Launch a Survey

Close up of a model of a campus buildingTo get to know their students, Professor Eric Demaine and his co-instructor gave students a survey during the first lecture of Algorithmic Lower Bounds: Fun with Hardness Proofs. The survey helped them understand the prior knowledge students brought to the course, along with students’ specific interests that could shape the curriculum, which was still being actively developed. “There were a few topics that stood out as particularly interesting to the students,” comments Demaine in his Instructor Insights video. “And then one thing I was curious about was the use of fun examples. I was worried that students would not take the material seriously if I only used fun examples. But the feedback I got was that a lot of people wanted to see games and puzzles . . . So I took that as permission to use a lot more fun examples . . . I used the survey to really get to know the students. And to see where they were coming from, and to help aim the class in a direction that would help them get the most out of it.”

  1. Create Student Profiles

A landfill with birds circling above it.D-Lab: Waste is an introductory course that provides students with a multidisciplinary approach to managing waste in low- and middle-income countries, with strategies that diminish greenhouse gas emissions and provide enterprise opportunities for marginalized populations. With 10 students in the course, co-instructors Kate Mytty and Pedro Reynolds-Cuellar used one-on-one check-ins to get to know students: “During our first check-in session,” notes Mytty in her Instructor Insights, “we asked students questions, such as, What brought you to this class? Why are you interested in waste? What do you hope to get out of this class? How can we help you get the most out of your learning experience? and What kind of resources can we send you throughout the semester that will help you explore waste through your own interests? As the semester progressed, our check-in sessions also involved conversations about students’ individualized final projects.”

“This approach for getting to know students,” she continues, “grew out of my experience serving as a teaching assistant with a colleague who was a very engaging educator. We had 25 students in our class and he created a profile for each student. The profile included information about the student’s major, interest in the course, career path, and the kinds of resources the student would find helpful. Every few weeks we sent students new resources based on their profiles. We also documented the resources we sent them. This system allowed us to develop personalized relationships with students and to provide them with an experience that extended beyond the explicit learning goals of the course”

Mytty says, “I found that intentionally creating similar opportunities to get to know students in  D-Lab: Waste was valuable for Pedro and I because it allowed us to learn from students’ expertise. Doing so also helped students understand that we, as the instructors, were deeply invested in their education, which is something I think is often missing from students’ post-secondary learning experiences.”

  1. Use Pizza (but you already knew that)

Graphic depiction of equations and bondsPiazza digital forums are great, but don’t neglect the in-person analogy joys of pizza. Professor Catherine Drennan uses pizza forums to connect with students in Principles of Chemical Science, her large lecture course. “The idea,” she notes in her Instructor Insights video, “was that in a big class of 300 students, most of the students are not going to have an opportunity to really meet the professors. They may go to office hours, but even then, you can’t schedule office hours at a time when all 300 people in the class are available . . . But with the pizza forums, which are every few weeks during the semester, students get to know the faculty and vice versa.” She goes on the explain that the pizza forums help the staff to learn about how students are experiencing the course, and how they are experiencing MIT, in general. Drennan says, “I love to ask them, ‘What is one thing about MIT that is exactly what you expected and what’s one thing that really surprised you when you got here?’ . . . It’s always a lot of fun to get to know them.”

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Have another great strategy for getting to know students? Share with your colleagues by posting an idea in the comments. And, thanks!

Spotlighting important (mini)figures in STEM: An interview with Maia Weinstock

By Sarah Hansen, OCW Educator Project Manager

Lego minifigure of scientist.

LEGO® figurine of Shirley Ann Jackson by Maia Weinstock. (Image courtesy of pixbymaia on flickr. License: BY-NC-SA.)

Women scientists and engineers have long played significant roles in shaping STEM disciplines and advancing technological innovation, yet many go unrecognized. (Case in point: How many women scientists can you name right now?) Maia Weinstock is committed to changing this. In the fall of 2017, she taught WGS.S10 History of Women in Science and Engineering, a course for MIT undergraduates that spotlighted the contributions of women in STEM and created space for uncovering how biases in academia and popular culture impact scientific achievements.

The course also had this: LEGO® minifigures depicting women scientists, created and photographed by Weinstock herself. (We know. History + LEGO Minifigures + Science = Where Can I Sign Up? Thanks, MIT, for being awesome and for sharing it all on MIT OpenCourseWare, for free.)

We interviewed Weinstock to learn about what inspired her to teach this course, how she helped students edit “the most popular encyclopedia in the world” to better include the achievements of women scientists, and of course, how she’s rocking the world of LEGO® minifigures with her depictions of scientists like chemical engineer Paula Hammond, and Johnson Space Center Director Ellen Ochoa. (Breaking news: Weinstein’s Women of NASA Lego® Prototype has just been added to the Smithsonian Air and Space Museum!). You can read excerpts from our interview below. Whether you’re an educator wanting to spotlight the role of women in STEM, a LEGO®s fan—or both—we think you’ll enjoy listening in on the conversation.

OCW: The history of women in science and engineering is an important (and often neglected) topic. What inspired you to teach the course?

Maia Weinstock: I’ve been interested in the topic for many years, and have worked on numerous writings and projects relating to the history of women in the STEM fields. The most well-known of these is a series of LEGO® minifigures I’ve been crafting and photographing featuring scientists and engineers. Four of these became part of a real set sold in stores in the late fall of 2017 (LEGO® Women of NASA). I wanted to teach the course as a way to impart the considerable knowledge I’ve amassed about this area over the years, and to give students a sense of MIT’s own history in relation to the women who have come through and made their mark.

Two women standing in an office. One woman is holding a LEGO minifigure.

Maia Weinstock (left) with Johnson Space Center director Ellen Ochoa and her LEGO minifigure. (Image by Maia pixbymaia on flickr. License: CC BY-NC-SA.).

OCW: You asked students to edit or add an article to Wikipedia about women in STEM. Tell us about your decision to develop this assignment.

Maia Weinstock: I have been a longtime contributor to Wikipedia, with the goal of improving the representation of women both on the pages of Wikipedia as well as behind the scenes as editors. We know through various surveys that 85 to 90 percent of Wikipedia editors are male, which means that only 10-15 percent of editors are women. Over the past 5 years I’ve organized quite a few edit-a-thons aimed at countering bias in terms of women’s representation, so I wanted to bring that kind of experience to the classroom. Our 3-hour class served as an abbreviated edit-a-thon: I prepared a class page on Wikipedia and facilitated both the selection of subjects that might work and the hands-on editing. In the end, each student did create a new article, so this gives participants a way to feel that they’re contributing directly to improving the most popular encyclopedia in the world—while giving recognition to an underappreciated woman in engineering or science.

OCW: As you noted above, you’ve done a lot of creative work with LEGO minifigures. Tell us more about this work and the role of LEGO®s in the course.

Maia Weinstock: I started creating LEGO®s in the likeness of scientists and engineers in early 2010, when I made one as a gift to my friend Carolyn Porco, who is a planetary scientist. I had been inspired by a minifigure of Ada Lovelace that I’d come across, but I wanted mine to depict current-day personalities because so few people can actually name a living scientist or engineer, much less a female one. Since then I’ve made over 100 of these figures of real individuals, taken photos and posted them to social media, and people have gotten a kick out of it. In 2012 I learned about the LEGO® Friends line, which was a major push to provide a product aimed squarely at girls. Unfortunately, the line was problematic in a number of ways, so I started learning more about the history of female minifigures and writing about the lack of female characters in LEGO®’s offerings, especially women in STEM professions. It seems like a fairly commonplace discussion in the media these days, but back in 2013 no one was talking about this. I actually broke the story of the first female lab scientist that LEGO came out with as part of their minifigures line, and I followed up with popular articles on diversity in the LEGO universe.

“I started creating LEGO®s in the likeness of scientists and engineers in early 2010 . . . I had been inspired by a minifigure of Ada Lovelace that I’d come across, but I wanted mine to depict current-day personalities because so few people can actually name a living scientist or engineer, much less a female one.” — MAIA WEINSTOCK

Around that same time, I learned about a crowdsourcing contest called LEGO® Ideas (originally known as Cuusoo) whereby people can suggest ideas for LEGO to consider making. I was an early champion of the Female Minifigures set that surfaced on that site, which was later rebranded the Research Institute; LEGO® chose to feature three scientists instead of women in very different professions. Anyway, I wanted to try suggesting ideas focused on actual women, since I’d been doing that for a few years already on my own at that point. My first go, a depiction of the four women who have been U.S. Supreme Court justices, unfortunately didn’t make it into the contest at all because it went against house rules about politics—but it went viral anyway when I shared photos on social media. A second try featuring women in bioengineering didn’t get much traction. But my third try, a set featuring five women in NASA history, was extremely successful, getting all 10,000 votes needed to be considered for the grand prize in just two weeks. A modified version of the set was released to the public last year and ended up shooting up to No. 1 on Amazon’s best-selling toy list on the first day it was available, and selling out its first printing very quickly. So that was fun.

LEGO minifigure depicting Dr. Paula T. Hammond.

MIT Chemical Engineering professor, Dr. Paula T. Hammond, depicted as a Lego® figurine. Dr. Hammond’s work concerns the use of electrostatics to generate functional materials with highly controlled architecture. (Image by pixbymaya on flickr. License: CC BY-NC-SA.)

In terms of LEGO®s in the course, I sprinkled my own LEGO® photos in with historic images of women who we were reading about and watching films about and listening to podcasts about. I found it was a great way to have fun with the subject, and students enjoyed figuring out which people the minifigs represented based on the physical characteristics of the LEGO® pieces I selected. Interestingly, one of my students for her final project did something similar except with Japanese-style crochet dolls: She crafted dolls of and then made photo essays featuring several STEM women in MIT history, including Shirley Ann Jackson, Millie Dresselhaus, and Sheila Widnall. It was awesome! Finally, I kept my class in the loop as we approached launch day for my Women of NASA LEGO® project, and most of the students attended a launch party I held at the LEGOLAND Discovery Center in nearby Somerville, which featured special guests Margaret Hamilton and Nancy Grace Roman, who are depicted in the set, and Bear Ride, the sister of Sally Ride, who is also in the set (but who passed away in 2012).

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You can read the complete interview with Maia Weinstock on the Instructor Insights page of her OCW course.

Keep learning! The following courses may be of interest to you:

More on Women in Science, Technology, and Academia 

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Does it matter in education whether or not you’ve got a Y chromosome? In this discussion-based seminar, students explore why males outrank females in math and science and career advancement (particularly in academia), and why girls get better grades and go to college more often than boys. This course explores if the sexes have different learn ing styles and if women are denied advanced opportunities in academia and the workforce.

More on LEGO®s

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This course covers the fundamental principles, practices and tools of Lean Six Sigma methods that underlay modern organizational productivity approaches applied in aerospace, automotive, health care, and other sectors. It includes lectures, active learning exercises, a plant tour, talks by industry practitioners, and videos. One third of the course is devoted to a physical simulation of an aircraft manufacturing enterprise using LEGO®s [PDF] or a clinic to illustrate the power of Lean Six Sigma methods.

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